Scanpy Analysis¶
In [1]:
%config InlineBackend.figure_formats = ["retina"] # increase resolution on retina screens
import numpy as np
import pandas as pd
import warnings
import sys
warnings.filterwarnings("ignore")
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
#import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors
import scanpy as sc
import seaborn as sns
import anndata as ad
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.n_jobs = 23
scanpy==1.9.1 anndata==0.9.2 umap==0.5.3 numpy==1.23.5 scipy==1.11.1 pandas==1.5.2 scikit-learn==1.3.0 statsmodels==0.14.0 python-igraph==0.11.5 pynndescent==0.5.10
In [2]:
from gtfparse import read_gtf
In [3]:
%load_ext rpy2.ipython
INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY INFO:rpy2.situation:R home found: /scratch/prj/sangrithi_lab/programs/miniconda3/envs/MS_Conda5/lib/R INFO:rpy2.situation:R library path: INFO:rpy2.situation:LD_LIBRARY_PATH: INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.
In [4]:
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)#None
In [5]:
plt.rcParams.update({})
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Comic Sans MS"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
sc.settings.set_figure_params(dpi=125, dpi_save=300, format="pdf", frameon=False, vector_friendly=True)
sc.settings.figdir = '/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/'
# Set context to `"paper"`
sns.set_context("paper", font_scale=1, rc={"font.size":8,"axes.labelsize":8,"axes.titlesize":14})
In [6]:
heatcolors_wr = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white", "#FFF5F0", "#FEE0D2", "#FCBBA1",
"#FC9272", "#FB6A4A", "#EF3B2C",
"#CB181D", "#A50F15", "#67000D"])
heatcolors = matplotlib.colors.LinearSegmentedColormap.from_list("", ["gray", "#000004FF", "#330A5FFF", "#781C6DFF",
"#BB3754FF", "#ED6925FF",
"#FCB519FF", "#FCFFA4FF"])
In [7]:
#%%R
#heatcolour_wr <- colorRampPalette(c("white", "#FFF5F0", "#FEE0D2", "#FCBBA1","#FC9272", "#FB6A4A", "#EF3B2C","#CB181D", "#A50F15", "#67000D"))(100)
#show_col(heatcolour_wr)
In [8]:
%%R
require(tidyverse)
require(dplyr)
require(tidyr)
options(mc.cores = 23L)
require(magrittr)
#library(scatterpie)
require(extrafont)
require(scales)
require(viridis)
require(viridisLite)
require(gridExtra)
options(dplyr.width = 200)
#options(repr.matrix.max.cols=500, repr.matrix.max.rows=100)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.1.2 ✔ readr 2.1.4 ✔ forcats 1.0.0 ✔ stringr 1.5.0 ✔ ggplot2 3.4.2 ✔ tibble 3.2.1 ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 ✔ purrr 1.0.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Loading required package: tidyverse
Loading required package: magrittr
Attaching package: ‘magrittr’
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Loading required package: extrafont
Registering fonts with R
Loading required package: scales
Attaching package: ‘scales’
The following object is masked from ‘package:purrr’:
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The following object is masked from ‘package:readr’:
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Loading required package: viridis
Loading required package: viridisLite
Attaching package: ‘viridis’
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Attaching package: ‘gridExtra’
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In [9]:
## Load the Mouse GTF file
gtf_df = read_gtf("/users/k2142342/Lab_Sangrithi/reference/annot/Mus_musculus.GRCm38/Mus_musculus.GRCm38.102.chr.gtf")
INFO:root:Extracted GTF attributes: ['gene_id', 'gene_version', 'gene_name', 'gene_source', 'gene_biotype', 'havana_gene', 'havana_gene_version', 'transcript_id', 'transcript_version', 'transcript_name', 'transcript_source', 'transcript_biotype', 'havana_transcript', 'havana_transcript_version', 'tag', 'transcript_support_level', 'exon_number', 'exon_id', 'exon_version', 'ccds_id', 'protein_id', 'protein_version']
In [10]:
genes = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
'antisense', 'lincRNA',
'TR_V_gene',
'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].gene_id.unique()
In [11]:
len(genes)
Out[11]:
31434
In [12]:
genes_df = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
'antisense', 'lincRNA',
'TR_V_gene',
'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].reset_index(drop = True)
In [13]:
genes_df
Out[13]:
| seqname | source | feature | start | end | score | strand | frame | gene_id | gene_version | gene_name | gene_source | gene_biotype | havana_gene | havana_gene_version | transcript_id | transcript_version | transcript_name | transcript_source | transcript_biotype | havana_transcript | havana_transcript_version | tag | transcript_support_level | exon_number | exon_id | exon_version | ccds_id | protein_id | protein_version | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | ensembl_havana | transcript | 3214482 | 3671498 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | CCDS14803 | |||||
| 1 | 1 | ensembl_havana | exon | 3670552 | 3671498 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | ENSMUSE00000485541 | 3 | CCDS14803 | ||
| 2 | 1 | ensembl_havana | CDS | 3670552 | 3671348 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | CCDS14803 | ENSMUSP00000070648 | 4 | ||
| 3 | 1 | ensembl_havana | start_codon | 3671346 | 3671348 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | CCDS14803 | ||||
| 4 | 1 | ensembl_havana | exon | 3421702 | 3421901 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 2 | ENSMUSE00000449517 | 3 | CCDS14803 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1387326 | MT | insdc | stop_codon | 13552 | 13554 | NaN | - | 0 | ENSMUSG00000064368 | 1 | mt-Nd6 | insdc | protein_coding | ENSMUST00000082419 | 1 | mt-Nd6-201 | insdc | protein_coding | basic | NA | 1 | |||||||||
| 1387327 | MT | insdc | transcript | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | ||||||||||
| 1387328 | MT | insdc | exon | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 | ENSMUSE00000521548 | 1 | |||||||
| 1387329 | MT | insdc | CDS | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 | ENSMUSP00000081003 | 1 | |||||||
| 1387330 | MT | insdc | start_codon | 14145 | 14147 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 |
1387331 rows × 30 columns
In [14]:
#genes_df.to_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv")
In [15]:
#genes_df = pd.read_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv", index_col=0)
In [16]:
genes = genes_df.gene_id.unique()
In [17]:
len(genes_df.gene_id[genes_df.seqname.isin(['MT'])].unique())
Out[17]:
13
In [18]:
%%R -o countdataSC
# Load the count data
countdataSC <- read.csv("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/gcAnndata_raw_x.csv", header=TRUE, row.names=1)
dim(countdataSC)
[1] 31434 822
In [19]:
%%R
colnames(countdataSC) %>% head() %>% print()
dim(countdataSC)
colnames(countdataSC) <- gsub("\\.", "_", colnames(countdataSC))
dim(countdataSC)
[1] "E10_GFP_pos_F1_10" "E10_GFP_pos_F1_11" "E10_GFP_pos_F1_12" [4] "E10_GFP_pos_F1_14" "E10_GFP_pos_F1_15" "E10_GFP_pos_F1_16" [1] 31434 822
In [20]:
%%R
colnames(countdataSC) %>% head() %>% print()
#head(countdataSC)
[1] "E10_GFP_pos_F1_10" "E10_GFP_pos_F1_11" "E10_GFP_pos_F1_12" [4] "E10_GFP_pos_F1_14" "E10_GFP_pos_F1_15" "E10_GFP_pos_F1_16"
In [21]:
%%R -o countdataSCMeta
countdataSCMeta <- read.csv("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/gcAnndata_obs.csv",
header = T,
row.names = 1)
countdataSCMeta <- countdataSCMeta[,-c(2,3,4,5,13,14)]
#rownames(countdataSCMeta) <- countdataSCMeta$sample
#colnames(countdataSCMeta)[1] = "Cell"
In [ ]:
In [22]:
%%R
countdataSCMeta %>% names() #group_by(age, sex) %>% tally()
[1] "Sample" "sex" "stage" [4] "Embryo" "celltype" "stage_sex" [7] "stage_sex_celltype" "CellID"
In [23]:
%%R -o countdataSC -o obs_names -o var_names -i genes
# subset rownames
countdataSC <- countdataSC[rownames(countdataSC) %in% genes, ]
obs_names <- colnames(countdataSC)
var_names <- rownames(countdataSC)
countdataSC <- as.matrix(countdataSC)
print(dim(countdataSC[rownames(countdataSC) %in% genes, ]))
[1] 31434 822
Genome1 and 2 counts¶
In [24]:
%%R -o countdataSC_g1 -o countdataSC_g2
countdataSC_g1 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Updated_counts/counts_genome1.txt", header=TRUE, row.names=1)
countdataSC_g2 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Updated_counts/counts_genome2.txt", header=TRUE, row.names=1)
countdataSC_g1 <- countdataSC_g1[,-c(1:5)]
countdataSC_g2 <- countdataSC_g2[,-c(1:5)]
In [25]:
%%R
colnames(countdataSC_g1) <- gsub("\\.", "_", colnames(countdataSC_g1))
colnames(countdataSC_g2) <- gsub("\\.", "_", colnames(countdataSC_g2))
dim(countdataSC_g1) %>% print()
dim(countdataSC_g2) %>% print()
[1] 55401 882 [1] 55401 882
In [26]:
%%R -o countdataSC_g1 -o obs_names_g1 -o var_names_g1 -i genes
# subset rownames
countdataSC_g1 <- countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]
obs_names_g1 <- colnames(countdataSC_g1)
var_names_g1 <- rownames(countdataSC_g1)
countdataSC_g1 <- as.matrix(countdataSC_g1)
print(dim(countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]))
[1] 31434 882
In [27]:
%%R
#head(countdataSC_g1)
colnames(countdataSC_g1) %in% rownames(countdataSCMeta) %>% table()
#colnames(countdataSC_g1)
. FALSE 882
In [28]:
%%R -o countdataSC_g2 -o obs_names_g2 -o var_names_g2 -i genes
# subset rownames
countdataSC_g2 <- countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]
obs_names_g2 <- colnames(countdataSC_g2)
var_names_g2 <- rownames(countdataSC_g2)
countdataSC_g2 <- as.matrix(countdataSC_g2)
print(dim(countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]))
[1] 31434 882
In [29]:
%%R
is.na(countdataSC_g1) %>% table()
is.na(countdataSC_g2) %>% table()
. FALSE 27724788
SCANPY¶
In [30]:
gcAnndata = sc.AnnData(X = countdataSC.T, obs = obs_names, var = var_names)
In [31]:
gcAnndata.obs.rename(columns={0:'Sample'}, inplace=True)
gcAnndata.var.rename(columns={0:'geneid'}, inplace=True)
In [32]:
gcAnndata.var.index = gcAnndata.var.geneid.to_list()
gcAnndata.obs.index = gcAnndata.obs.Sample.to_list()
In [33]:
annot = sc.queries.biomart_annotations(
"mmusculus",
["ensembl_gene_id", "start_position", "end_position", "chromosome_name", "external_gene_name"],
host="nov2020.archive.ensembl.org",
use_cache='TRUE',
).set_index("ensembl_gene_id")
In [34]:
gcAnndata.var[annot.columns] = annot
In [35]:
gcAnndata.var.index = gcAnndata.var.geneid.tolist()
In [36]:
gcAnndata
Out[36]:
AnnData object with n_obs × n_vars = 822 × 31434
obs: 'Sample'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name'
In [37]:
gcAnndata.var_names_make_unique()
In [38]:
gcAnndata.n_obs
Out[38]:
822
In [39]:
gcAnndata.var
Out[39]:
| geneid | start_position | end_position | chromosome_name | external_gene_name | |
|---|---|---|---|---|---|
| ENSMUSG00000051951 | ENSMUSG00000051951 | 3205901 | 3671498 | 1 | Xkr4 |
| ENSMUSG00000089699 | ENSMUSG00000089699 | 3466587 | 3513553 | 1 | Gm1992 |
| ENSMUSG00000102343 | ENSMUSG00000102343 | 3905739 | 3986215 | 1 | Gm37381 |
| ENSMUSG00000025900 | ENSMUSG00000025900 | 3999557 | 4409241 | 1 | Rp1 |
| ENSMUSG00000025902 | ENSMUSG00000025902 | 4490931 | 4497354 | 1 | Sox17 |
| ... | ... | ... | ... | ... | ... |
| ENSMUSG00000065947 | ENSMUSG00000065947 | 9877 | 10173 | MT | mt-Nd4l |
| ENSMUSG00000064363 | ENSMUSG00000064363 | 10167 | 11544 | MT | mt-Nd4 |
| ENSMUSG00000064367 | ENSMUSG00000064367 | 11742 | 13565 | MT | mt-Nd5 |
| ENSMUSG00000064368 | ENSMUSG00000064368 | 13552 | 14070 | MT | mt-Nd6 |
| ENSMUSG00000064370 | ENSMUSG00000064370 | 14145 | 15288 | MT | mt-Cytb |
31434 rows × 5 columns
In [40]:
gcAnndata.var.chromosome_name.str.startswith('MT')
Out[40]:
ENSMUSG00000051951 False
ENSMUSG00000089699 False
ENSMUSG00000102343 False
ENSMUSG00000025900 False
ENSMUSG00000025902 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: chromosome_name, Length: 31434, dtype: bool
In [41]:
gcAnndata.var['mt'] = gcAnndata.var.chromosome_name=='MT' # annotate the group of mitochondrial genes as 'mt'
gcAnndata.var['mt']
Out[41]:
ENSMUSG00000051951 False
ENSMUSG00000089699 False
ENSMUSG00000102343 False
ENSMUSG00000025900 False
ENSMUSG00000025902 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: mt, Length: 31434, dtype: bool
In [42]:
gcAnndata.var.chromosome_name.str.startswith('MT')
Out[42]:
ENSMUSG00000051951 False
ENSMUSG00000089699 False
ENSMUSG00000102343 False
ENSMUSG00000025900 False
ENSMUSG00000025902 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: chromosome_name, Length: 31434, dtype: bool
In [43]:
sc.pp.calculate_qc_metrics(gcAnndata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)
In [44]:
gcAnndata.obs
Out[44]:
| Sample | n_genes_by_counts | total_counts | total_counts_mt | pct_counts_mt | |
|---|---|---|---|---|---|
| E10_GFP_pos_F1_10 | E10_GFP_pos_F1_10 | 8707 | 715292 | 918 | 0.128339 |
| E10_GFP_pos_F1_11 | E10_GFP_pos_F1_11 | 8473 | 1564689 | 5792 | 0.370169 |
| E10_GFP_pos_F1_12 | E10_GFP_pos_F1_12 | 8206 | 2310070 | 3992 | 0.172809 |
| E10_GFP_pos_F1_14 | E10_GFP_pos_F1_14 | 8338 | 1320374 | 5065 | 0.383603 |
| E10_GFP_pos_F1_15 | E10_GFP_pos_F1_15 | 8473 | 1283668 | 3195 | 0.248896 |
| ... | ... | ... | ... | ... | ... |
| M4_GFPP_P2_B5 | M4_GFPP_P2_B5 | 5349 | 1735796 | 15873 | 0.914451 |
| M4_GFPP_P2_B7 | M4_GFPP_P2_B7 | 6595 | 2647587 | 22935 | 0.866260 |
| M4_GFPP_P2_B8 | M4_GFPP_P2_B8 | 5555 | 1920124 | 20754 | 1.080868 |
| M4_GFPP_P2_B9 | M4_GFPP_P2_B9 | 5391 | 1287828 | 9147 | 0.710266 |
| M4_GFPP_P2_D2 | M4_GFPP_P2_D2 | 6042 | 3026218 | 22180 | 0.732928 |
822 rows × 5 columns
In [45]:
countdataSCMeta
Out[45]:
| Sample | sex | stage | Embryo | celltype | stage_sex | stage_sex_celltype | CellID | |
|---|---|---|---|---|---|---|---|---|
| E10_GFP_pos_F1_10 | E10_GFP_pos_F1_10 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_10 |
| E10_GFP_pos_F1_11 | E10_GFP_pos_F1_11 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_11 |
| E10_GFP_pos_F1_12 | E10_GFP_pos_F1_12 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_12 |
| E10_GFP_pos_F1_14 | E10_GFP_pos_F1_14 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_14 |
| E10_GFP_pos_F1_15 | E10_GFP_pos_F1_15 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_15 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| E115_GFP_neg_M5_5 | E115_GFP_neg_M5_5 | male | E11.5 | M5 | somatic_cell | male_E11.5 | male_E11.5_somatic_cell | E115_GFP_neg_M5_5 |
| E115_GFP_neg_M5_6 | E115_GFP_neg_M5_6 | male | E11.5 | M5 | somatic_cell | male_E11.5 | male_E11.5_somatic_cell | E115_GFP_neg_M5_6 |
| E115_GFP_neg_M5_7 | E115_GFP_neg_M5_7 | male | E11.5 | M5 | somatic_cell | male_E11.5 | male_E11.5_somatic_cell | E115_GFP_neg_M5_7 |
| E115_GFP_neg_M5_8 | E115_GFP_neg_M5_8 | male | E11.5 | M5 | somatic_cell | male_E11.5 | male_E11.5_somatic_cell | E115_GFP_neg_M5_8 |
| E115_GFP_neg_M5_9 | E115_GFP_neg_M5_9 | male | E11.5 | M5 | somatic_cell | male_E11.5 | male_E11.5_somatic_cell | E115_GFP_neg_M5_9 |
822 rows × 8 columns
In [46]:
gcAnndata.obs['sex'] = countdataSCMeta['sex']
In [47]:
gcAnndata.obs['stage'] = countdataSCMeta['stage']
In [48]:
gcAnndata.obs['Embryo'] = countdataSCMeta['Embryo']
gcAnndata.obs['celltype'] = countdataSCMeta['celltype']
In [49]:
gcAnndata.obs['stage_sex'] = gcAnndata.obs.sex.astype('str') + "_" + gcAnndata.obs.stage.astype('str')
gcAnndata.obs['stage_sex_celltype'] = gcAnndata.obs.stage_sex.astype('str') + "_" + gcAnndata.obs.celltype.astype('str')
In [50]:
#pd.set_option('display.max_rows', 10)
gcAnndata.obs
Out[50]:
| Sample | n_genes_by_counts | total_counts | total_counts_mt | pct_counts_mt | sex | stage | Embryo | celltype | stage_sex | stage_sex_celltype | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| E10_GFP_pos_F1_10 | E10_GFP_pos_F1_10 | 8707 | 715292 | 918 | 0.128339 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell |
| E10_GFP_pos_F1_11 | E10_GFP_pos_F1_11 | 8473 | 1564689 | 5792 | 0.370169 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell |
| E10_GFP_pos_F1_12 | E10_GFP_pos_F1_12 | 8206 | 2310070 | 3992 | 0.172809 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell |
| E10_GFP_pos_F1_14 | E10_GFP_pos_F1_14 | 8338 | 1320374 | 5065 | 0.383603 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell |
| E10_GFP_pos_F1_15 | E10_GFP_pos_F1_15 | 8473 | 1283668 | 3195 | 0.248896 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| M4_GFPP_P2_B5 | M4_GFPP_P2_B5 | 5349 | 1735796 | 15873 | 0.914451 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell |
| M4_GFPP_P2_B7 | M4_GFPP_P2_B7 | 6595 | 2647587 | 22935 | 0.866260 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell |
| M4_GFPP_P2_B8 | M4_GFPP_P2_B8 | 5555 | 1920124 | 20754 | 1.080868 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell |
| M4_GFPP_P2_B9 | M4_GFPP_P2_B9 | 5391 | 1287828 | 9147 | 0.710266 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell |
| M4_GFPP_P2_D2 | M4_GFPP_P2_D2 | 6042 | 3026218 | 22180 | 0.732928 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell |
822 rows × 11 columns
In [51]:
#gcAnndata[gcAnndata.obs['Sample'].isin(['E145_2F_P2_B4', 'E145_2F_P2_B5', 'E145_2F_P2_B6', 'E125_5F_GFPP_B11'])].obs.head()
#gcAnndata[gcAnndata.obs['stage'].isin(['E16_5'])].obs.head()
gcAnndata
Out[51]:
AnnData object with n_obs × n_vars = 822 × 31434
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
In [52]:
gcAnndata.obs['stage_sex_celltype'].value_counts()
Out[52]:
female_E16.5_germ_cell 71 male_E16.5_germ_cell 67 male_E15.5_germ_cell 65 male_E14.5_germ_cell 48 female_E12.5_germ_cell 48 female_E14.5_germ_cell 48 female_E10.5_germ_cell 47 female_E15.5_germ_cell 47 male_E9.5_germ_cell 47 female_E11.5_germ_cell 47 female_E9.5_germ_cell 46 male_E13.5_germ_cell 45 male_E11.5_germ_cell 44 female_E13.5_germ_cell 40 male_E12.5_germ_cell 40 male_E10.5_germ_cell 24 male_E11.5_somatic_cell 24 female_E11.5_somatic_cell 24 Name: stage_sex_celltype, dtype: int64
In [53]:
gcAnndata.obs['stage_sex'].value_counts()
Out[53]:
female_E11.5 71 female_E16.5 71 male_E11.5 68 male_E16.5 67 male_E15.5 65 female_E12.5 48 male_E14.5 48 female_E14.5 48 female_E10.5 47 female_E15.5 47 male_E9.5 47 female_E9.5 46 male_E13.5 45 male_E12.5 40 female_E13.5 40 male_E10.5 24 Name: stage_sex, dtype: int64
In [54]:
gcAnndata.obs['sex'].head()
Out[54]:
E10_GFP_pos_F1_10 female E10_GFP_pos_F1_11 female E10_GFP_pos_F1_12 female E10_GFP_pos_F1_14 female E10_GFP_pos_F1_15 female Name: sex, dtype: object
In [55]:
gcAnndata.obs['CellID'] = gcAnndata.obs.index.tolist()
In [56]:
gcAnndata.var['Gene'] = gcAnndata.var.index.tolist()
In [57]:
sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
jitter=0.4, multi_panel=True)
... storing 'sex' as categorical ... storing 'stage' as categorical ... storing 'Embryo' as categorical ... storing 'celltype' as categorical ... storing 'stage_sex' as categorical ... storing 'stage_sex_celltype' as categorical ... storing 'chromosome_name' as categorical ... storing 'external_gene_name' as categorical
In [58]:
sc.pp.filter_cells(gcAnndata, min_genes = 3500)
sc.pp.filter_cells(gcAnndata, max_genes = 10000)
sc.pp.filter_cells(gcAnndata, min_counts = 500000)
sc.pp.filter_genes(gcAnndata, min_cells = 5)
gcAnndata = gcAnndata[gcAnndata.obs['pct_counts_mt'] < 5, :]
filtered out 9596 genes that are detected in less than 5 cells
In [59]:
mito_genes = gcAnndata.var.chromosome_name.str.startswith('MT')
mito_genes
Out[59]:
ENSMUSG00000051951 False
ENSMUSG00000102343 False
ENSMUSG00000025902 False
ENSMUSG00000033845 False
ENSMUSG00000025903 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: chromosome_name, Length: 21838, dtype: bool
In [60]:
keep_genes = np.invert(mito_genes)
gcAnndata = gcAnndata[:,keep_genes]
In [61]:
gcAnndata.raw = gcAnndata
In [62]:
gcAnndata_germ = gcAnndata[gcAnndata.obs["celltype"] == "germ_cell"]
gcAnndata_som = gcAnndata[gcAnndata.obs["celltype"] == "somatic_cell"]
In [63]:
gcAnndata
Out[63]:
AnnData object with n_obs × n_vars = 822 × 21827
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells'
In [64]:
sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
jitter=0.4, multi_panel=True)
In [65]:
plt.rcParams['figure.figsize'] = [5, 5]
In [66]:
sc.pp.normalize_total(gcAnndata, exclude_highly_expressed = True, max_fraction = 0.10, target_sum=1e4)
normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
['ENSMUSG00000052187', 'ENSMUSG00000055609', 'ENSMUSG00000069919']
finished (0:00:00)
In [67]:
sc.pp.normalize_total(gcAnndata_germ, exclude_highly_expressed = True, max_fraction = 0.10, target_sum=1e4)
normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
[]
finished (0:00:00)
In [68]:
sc.pp.log1p(gcAnndata)
In [69]:
sc.pp.log1p(gcAnndata_germ)
In [70]:
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="pct_counts_mt", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/pct_mito_germ.pdf", dpi = 300)
In [71]:
plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="pct_counts_mt", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/pct_mito_soma.pdf", dpi = 300)
In [72]:
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="n_genes_by_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/n_genes_germ.pdf", dpi = 300)
In [73]:
plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="n_genes_by_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/n_genes_soma.pdf", dpi = 300)
In [74]:
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="total_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/total_counts_germ.pdf", dpi = 300)
In [75]:
plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="total_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/total_counts_soma.pdf", dpi = 300)
In [76]:
plt.rcParams['figure.figsize'] = [4, 4]
In [77]:
sc.pp.highly_variable_genes(gcAnndata, flavor='seurat_v3', n_top_genes=800)
If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
'highly_variable', boolean vector (adata.var)
'highly_variable_rank', float vector (adata.var)
'means', float vector (adata.var)
'variances', float vector (adata.var)
'variances_norm', float vector (adata.var)
In [78]:
sc.pp.highly_variable_genes(gcAnndata_germ, flavor='seurat_v3', n_top_genes=800)
If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
'highly_variable', boolean vector (adata.var)
'highly_variable_rank', float vector (adata.var)
'means', float vector (adata.var)
'variances', float vector (adata.var)
'variances_norm', float vector (adata.var)
In [79]:
gcAnndata_germ.var['highly_variable'].value_counts()
Out[79]:
False 21027 True 800 Name: highly_variable, dtype: int64
In [80]:
sc.pl.highly_variable_genes(gcAnndata_germ)
In [81]:
sc.tl.pca(gcAnndata_germ, svd_solver='arpack')
computing PCA
on highly variable genes
with n_comps=50
finished (0:00:00)
In [82]:
sc.pl.pca(gcAnndata_germ, color=['stage_sex'], size=200, ec="black", linewidth=0.5, frameon=True)#
In [ ]:
In [83]:
sc.pp.neighbors(gcAnndata, n_neighbors=5, n_pcs=50)
computing neighbors
WARNING: You’re trying to run this on 21827 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
computing PCA
with n_comps=50
finished (0:00:00)
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
In [84]:
sc.pp.neighbors(gcAnndata_germ, n_neighbors=5, n_pcs=50)
computing neighbors
using 'X_pca' with n_pcs = 50
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
In [85]:
sc.tl.leiden(gcAnndata_germ, resolution = 0.7, random_state = 55063) # Perform clustering
running Leiden clustering
finished: found 11 clusters and added
'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
In [86]:
sc.tl.umap(gcAnndata_germ, random_state = 55063)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:00)
In [87]:
sc.pl.umap(gcAnndata_germ, color=['stage_sex'], size=100, ec="black", linewidth=0.5, frameon=True)
In [88]:
sc.tl.tsne(gcAnndata_germ, n_pcs=50, perplexity=41, learning_rate=1000, random_state = 55063)
computing tSNE
using 'X_pca' with n_pcs = 50
using sklearn.manifold.TSNE
finished: added
'X_tsne', tSNE coordinates (adata.obsm) (0:00:01)
In [89]:
#plt.rcParams['figure.figsize'] = [5,5]
sc.pl.tsne(gcAnndata_germ, color=['stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True)#, save = '_stage_sex.png')
In [90]:
sc.pl.tsne(gcAnndata_germ, color=["leiden", 'stage_sex', 'Embryo'],legend_loc="on data", size=150, edgecolor="black", linewidths=0.5, frameon=True, legend_fontsize=8, legend_fontoutline=1)
In [91]:
sc.pl.tsne(gcAnndata_germ, color=['sex', 'stage', 'stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True, show=False)#, save="_multi.png")
Out[91]:
[<AxesSubplot: title={'center': 'sex'}, xlabel='tSNE1', ylabel='tSNE2'>,
<AxesSubplot: title={'center': 'stage'}, xlabel='tSNE1', ylabel='tSNE2'>,
<AxesSubplot: title={'center': 'stage_sex'}, xlabel='tSNE1', ylabel='tSNE2'>]
In [92]:
# colour scheme for nice plots
colours = ["#f1eef6", "#d8d6dd", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f",
"#eff3ff", "#d7dae5", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"]
cats = ["male_E9.5", "male_E10.5", "male_E11.5", "male_E12.5", "male_E13.5", "male_E14.5", "male_E15.5", "male_E16.5",
"female_E9.5", "female_E10.5", "female_E11.5", "female_E12.5", "female_E13.5", "female_E14.5", "female_E15.5", "female_E16.5"]
In [93]:
sc.pl.tsne(gcAnndata_germ, color = ['stage_sex'], palette = colours, size = 150, ec = "black", linewidth = 0.5, frameon = True)#, save = '_stage_sex.pdf')
In [94]:
gcAnndata_germ.obs.Sample = gcAnndata_germ.obs.Sample.astype('category')
In [95]:
sc.pl.pca(gcAnndata_germ, color='stage_sex', size=300, ec="black", frameon=True, legend_loc="on data", legend_fontsize=4, linewidth=0.2)
In [96]:
sc.pl.pca(gcAnndata_germ, color='stage_sex', size=800, ec="black", frameon=True, legend_fontsize=4, linewidth=0.2, projection='3d')
In [97]:
gcAnndata.obs
Out[97]:
| Sample | n_genes_by_counts | total_counts | total_counts_mt | pct_counts_mt | sex | stage | Embryo | celltype | stage_sex | stage_sex_celltype | CellID | n_genes | n_counts | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| E10_GFP_pos_F1_10 | E10_GFP_pos_F1_10 | 8707 | 715292 | 918 | 0.128339 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_10 | 8707 | 715292 |
| E10_GFP_pos_F1_11 | E10_GFP_pos_F1_11 | 8473 | 1564689 | 5792 | 0.370169 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_11 | 8473 | 1564689 |
| E10_GFP_pos_F1_12 | E10_GFP_pos_F1_12 | 8206 | 2310070 | 3992 | 0.172809 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_12 | 8206 | 2310070 |
| E10_GFP_pos_F1_14 | E10_GFP_pos_F1_14 | 8338 | 1320374 | 5065 | 0.383603 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_14 | 8338 | 1320374 |
| E10_GFP_pos_F1_15 | E10_GFP_pos_F1_15 | 8473 | 1283668 | 3195 | 0.248896 | female | E10.5 | F1 | germ_cell | female_E10.5 | female_E10.5_germ_cell | E10_GFP_pos_F1_15 | 8473 | 1283668 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| M4_GFPP_P2_B5 | M4_GFPP_P2_B5 | 5349 | 1735796 | 15873 | 0.914451 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell | M4_GFPP_P2_B5 | 5349 | 1735796 |
| M4_GFPP_P2_B7 | M4_GFPP_P2_B7 | 6595 | 2647587 | 22935 | 0.866260 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell | M4_GFPP_P2_B7 | 6595 | 2647587 |
| M4_GFPP_P2_B8 | M4_GFPP_P2_B8 | 5555 | 1920124 | 20754 | 1.080868 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell | M4_GFPP_P2_B8 | 5555 | 1920124 |
| M4_GFPP_P2_B9 | M4_GFPP_P2_B9 | 5391 | 1287828 | 9147 | 0.710266 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell | M4_GFPP_P2_B9 | 5391 | 1287828 |
| M4_GFPP_P2_D2 | M4_GFPP_P2_D2 | 6042 | 3026218 | 22180 | 0.732928 | male | E12.5 | M4 | germ_cell | male_E12.5 | male_E12.5_germ_cell | M4_GFPP_P2_D2 | 6042 | 3026218 |
822 rows × 14 columns
In [98]:
gcAnndata.obs.Sample = gcAnndata.obs.Sample.astype('category')
In [99]:
sc.tl.dendrogram(gcAnndata, groupby='stage_sex_celltype', optimal_ordering = 'True')
using 'X_pca' with n_pcs = 50 Storing dendrogram info using `.uns['dendrogram_stage_sex_celltype']`
In [100]:
sc.pl.correlation_matrix(gcAnndata, groupby="stage_sex_celltype", figsize=[5,4])
In [101]:
gcAnndata_f = gcAnndata_germ[gcAnndata_germ.obs["sex"] == 'female']
In [102]:
gcAnndata_germ[:,gcAnndata_germ.var[gcAnndata_germ.var.chromosome_name == 'X'].geneid]
Out[102]:
View of AnnData object with n_obs × n_vars = 774 × 755
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors'
obsm: 'X_pca', 'X_umap', 'X_tsne'
varm: 'PCs'
obsp: 'distances', 'connectivities'
In [103]:
Xgenes = gcAnndata_germ.var.index[gcAnndata_germ.var.chromosome_name=="X"].to_list()
C9genes = gcAnndata_germ.var.index[gcAnndata_germ.var.chromosome_name=="9"].to_list()
Agenes = gcAnndata_germ.var.index[~gcAnndata_germ.var.chromosome_name.isin(['X', 'Y', 'mt'])].to_list()
In [104]:
len(Xgenes)
Out[104]:
755
In [105]:
gcAnndata_germ.obs["Chr X"] = (np.mean(gcAnndata_germ[:, Xgenes].X, axis=1) / np.mean(gcAnndata_germ[:, Agenes].X, axis=1)).tolist()
gcAnndata_germ.obs["Chr 9"] = (np.mean(gcAnndata_germ[:, C9genes].X, axis=1) / np.mean(gcAnndata_germ[:, Agenes].X, axis=1)).tolist()
In [106]:
germXA_data = gcAnndata_germ.obs["stage_sex"].to_frame().join(gcAnndata_germ.obs["Chr 9"]).join(gcAnndata_germ.obs["Chr X"])
#Melt the data to long format
germXA_data_melted = germXA_data.melt(id_vars="stage_sex", var_name="key", value_name="value")
In [107]:
germXA_data_melted.groupby(['stage_sex', 'key']).describe()
Out[107]:
| value | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| count | mean | std | min | 25% | 50% | 75% | max | ||
| stage_sex | key | ||||||||
| female_E9.5 | Chr 9 | 46.0 | 1.039709 | 0.025462 | 0.984258 | 1.024952 | 1.038626 | 1.057714 | 1.084630 |
| Chr X | 46.0 | 0.803227 | 0.068857 | 0.661499 | 0.759838 | 0.796646 | 0.848185 | 1.011548 | |
| female_E10.5 | Chr 9 | 47.0 | 1.018178 | 0.026375 | 0.964124 | 0.996394 | 1.016021 | 1.039820 | 1.083480 |
| Chr X | 47.0 | 0.827360 | 0.071003 | 0.642227 | 0.786770 | 0.829078 | 0.882635 | 0.969546 | |
| female_E11.5 | Chr 9 | 47.0 | 1.034468 | 0.026669 | 0.980696 | 1.017420 | 1.031443 | 1.048950 | 1.105648 |
| Chr X | 47.0 | 0.850550 | 0.065388 | 0.656015 | 0.804692 | 0.866161 | 0.893809 | 0.980085 | |
| female_E12.5 | Chr 9 | 48.0 | 1.004120 | 0.046151 | 0.900822 | 0.978298 | 1.004921 | 1.022318 | 1.176105 |
| Chr X | 48.0 | 0.894220 | 0.093885 | 0.675559 | 0.832109 | 0.890431 | 0.965558 | 1.110798 | |
| female_E13.5 | Chr 9 | 40.0 | 1.050803 | 0.044856 | 0.935329 | 1.027651 | 1.061018 | 1.080815 | 1.128320 |
| Chr X | 40.0 | 0.998158 | 0.121724 | 0.738714 | 0.915549 | 0.983358 | 1.086108 | 1.403641 | |
| female_E14.5 | Chr 9 | 48.0 | 1.057175 | 0.051442 | 0.948005 | 1.020301 | 1.059237 | 1.081211 | 1.186694 |
| Chr X | 48.0 | 1.068013 | 0.122933 | 0.877402 | 0.975222 | 1.056255 | 1.146100 | 1.383988 | |
| female_E15.5 | Chr 9 | 47.0 | 1.043672 | 0.040359 | 0.948302 | 1.016188 | 1.047597 | 1.067518 | 1.142354 |
| Chr X | 47.0 | 1.040285 | 0.102518 | 0.817350 | 0.978810 | 1.031370 | 1.088123 | 1.360234 | |
| female_E16.5 | Chr 9 | 71.0 | 1.045216 | 0.046085 | 0.915336 | 1.023229 | 1.048614 | 1.067764 | 1.255449 |
| Chr X | 71.0 | 1.046350 | 0.111389 | 0.757019 | 0.974011 | 1.050920 | 1.131289 | 1.263218 | |
| male_E9.5 | Chr 9 | 47.0 | 1.037535 | 0.027919 | 0.984775 | 1.018983 | 1.034444 | 1.056644 | 1.116604 |
| Chr X | 47.0 | 0.777054 | 0.059360 | 0.660179 | 0.745140 | 0.777877 | 0.799905 | 0.986266 | |
| male_E10.5 | Chr 9 | 24.0 | 1.046189 | 0.063459 | 0.977798 | 1.008437 | 1.027262 | 1.050145 | 1.227075 |
| Chr X | 24.0 | 0.778304 | 0.080767 | 0.598791 | 0.741982 | 0.780417 | 0.833423 | 0.912178 | |
| male_E11.5 | Chr 9 | 44.0 | 1.020873 | 0.033534 | 0.897119 | 1.004651 | 1.021623 | 1.040349 | 1.107649 |
| Chr X | 44.0 | 0.764596 | 0.052674 | 0.665379 | 0.733369 | 0.753785 | 0.806336 | 0.926515 | |
| male_E12.5 | Chr 9 | 40.0 | 1.007538 | 0.035555 | 0.940206 | 0.987942 | 1.007853 | 1.031149 | 1.090051 |
| Chr X | 40.0 | 0.695224 | 0.046428 | 0.594265 | 0.670286 | 0.691706 | 0.724281 | 0.824372 | |
| male_E13.5 | Chr 9 | 45.0 | 1.003224 | 0.042403 | 0.892110 | 0.981161 | 1.004231 | 1.035544 | 1.085701 |
| Chr X | 45.0 | 0.715640 | 0.078493 | 0.525082 | 0.683332 | 0.711432 | 0.770313 | 0.852518 | |
| male_E14.5 | Chr 9 | 48.0 | 1.029505 | 0.027309 | 0.983100 | 1.007506 | 1.026058 | 1.048771 | 1.091244 |
| Chr X | 48.0 | 0.789000 | 0.060126 | 0.655340 | 0.749864 | 0.796295 | 0.826562 | 0.944256 | |
| male_E15.5 | Chr 9 | 65.0 | 1.009524 | 0.031620 | 0.941515 | 0.987887 | 1.009326 | 1.030966 | 1.091572 |
| Chr X | 65.0 | 0.781767 | 0.069406 | 0.616910 | 0.744473 | 0.771530 | 0.814544 | 1.122267 | |
| male_E16.5 | Chr 9 | 67.0 | 1.039657 | 0.038145 | 0.960685 | 1.014215 | 1.038578 | 1.061313 | 1.197431 |
| Chr X | 67.0 | 0.663442 | 0.048090 | 0.532843 | 0.633841 | 0.661075 | 0.692989 | 0.776138 | |
In [108]:
plt.rcParams["figure.figsize"]=4,4
bx = sns.violinplot(x="stage_sex", y="value",
hue="key", palette=["#0D8DB3","#C70039"],fliersize=0.1,linewidth=0.5,split=True,scale="count",inner="quartile",
data=germXA_data_melted)
bx.set(xlabel="")
bx.set_ylabel("Chromosome:Autosome ratio", fontsize=8)
bx.set_xticklabels(bx.get_xticklabels(), rotation=40, fontsize=8,horizontalalignment="right", rotation_mode="anchor", visible=True)
bx.grid(linestyle='-', linewidth='0.1')
bx.tick_params(
axis="both", # changes apply to the x-axis
which="major", # both major and minor ticks are affected
bottom=True, # ticks along the bottom edge are off
top=False, left=True, labeltop=False, # ticks along the top edge are off
labelbottom=True, pad=0.5, length=1.5)
bx.axhline(y=0.5,color='gray',linestyle='--')
bx.axhline(y=1,color='gray',linestyle='--')
plt.legend(loc="upper right", fontsize=6, frameon=False)
plt.tight_layout()
#figure = svm.get_figure()
#plt.savefig('.../X_Aratios.pdf', dpi=600)
Stats
In [109]:
from scipy import stats
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr X'])].value)
Out[109]:
MannwhitneyuResult(statistic=91274.0, pvalue=2.937609771698074e-29)
In [110]:
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr 9'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr 9'])].value)
Out[110]:
MannwhitneyuResult(statistic=137361.0, pvalue=0.005546971119452869)
In [111]:
# plt.rcParams["figure.figsize"]=10,3
#p = sns.scatterplot(gcAnndata.obs.Sample, gcAnndata.obs['Chr X'], hue=gcAnndata.obs.sex, legend=False)
# plt.setp(p.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor");
In [112]:
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value)
# At E14.5, female X output is significantly different to E14.5 male
Out[112]:
MannwhitneyuResult(statistic=3446.0, pvalue=1.3128901010109332e-06)
In [113]:
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female 9 output is NOT significantly different to E14.5 male
Out[113]:
MannwhitneyuResult(statistic=2738.0, pvalue=0.06617788001630687)
In [114]:
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female X output is NOT significantly different to E14.5 female 9 output
Out[114]:
MannwhitneyuResult(statistic=1126.0, pvalue=0.8517746681220008)
In [115]:
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, male X output is significantly different to E14.5 male 9 output
Out[115]:
MannwhitneyuResult(statistic=2453.0, pvalue=2.1919850389139528e-08)
PAGA
In [116]:
sc.tl.paga(gcAnndata_germ, groups="stage_sex")
#sc.tl.paga(gcAnndata_germ, groups="stage_sex_celltype")
running PAGA
finished: added
'paga/connectivities', connectivities adjacency (adata.uns)
'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
In [117]:
sc.pl.paga(gcAnndata_germ, threshold = 0.1, labels=None)
sc.pl.paga(gcAnndata_germ,
threshold = 0.2565,
color = "stage_sex",
fontsize=4,
fontoutline=1)#,
#layout='fa',
#save='_PAGA.pdf')
--> added 'pos', the PAGA positions (adata.uns['paga'])
--> added 'pos', the PAGA positions (adata.uns['paga'])
In [118]:
gcAnndata_germ.uns['iroot'] = np.flatnonzero(gcAnndata_germ.obs["stage_sex"] == "male_E9.5")[0]
In [119]:
sc.tl.dpt(gcAnndata_germ,
n_branchings = 1)
WARNING: Trying to run `tl.dpt` without prior call of `tl.diffmap`. Falling back to `tl.diffmap` with default parameters.
computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
finished (0:00:00)
eigenvalues of transition matrix
[1. 0.9991412 0.9982408 0.9951082 0.9941651 0.9870595
0.9859078 0.97948915 0.9767556 0.9663165 0.96494716 0.9602786
0.9577897 0.9386192 0.9367913 ]
finished: added
'X_diffmap', diffmap coordinates (adata.obsm)
'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)
computing Diffusion Pseudotime using n_dcs=10
finished: added
'dpt_pseudotime', the pseudotime (adata.obs)
'dpt_groups', the branching subgroups of dpt (adata.obs)
'dpt_order', cell order (adata.obs) (0:00:00)
In [120]:
sc.pl.tsne(gcAnndata_germ, color=["dpt_pseudotime", "stage_sex"], size=200,frameon=True, ec="black", linewidth=0.4)
sc.pl.tsne(gcAnndata_germ, color=["dpt_pseudotime"], size=200,frameon=True, ec="black", linewidth=0.4)#, save = '_pseudotime.pdf')
DE gene and marker analysis¶
In [121]:
sc.pl.matrixplot(gcAnndata_germ, var_names=["Eif2s3y", "Ddx3y", "Uty", "Usp9y", "Uba1y", "Kdm5d", "Sly", "Zfy2"],
gene_symbols="external_gene_name",
cmap=heatcolors_wr,
use_raw=False,
groupby="stage_sex",
#dendrogram = True,
swap_axes = True,
standard_scale="var"),
#save = '.../_Ygenes.png')
Out[121]:
(None,)
In [122]:
#gcAnndata_germ.var
sc.pl.tsne(gcAnndata_germ,
color=["Tfap2c", "Prdm14", "Pou5f1", "Nanog", "Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2", "Lefty1", "Lefty2", "Etv1",
"Pitx2", "Hesx1", "Egr4", "Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11", "Atr", "Atm"],
gene_symbols = 'external_gene_name',
use_raw=False,
legend_fontsize=6,
size=60,
edgecolor="black",
linewidths=0.1,
wspace=0.05,
hspace=0.2,
cmap=heatcolors,
ncols=7)#,
#save="_male_female_candidategenes")
In [123]:
#gcAnndata_germ.var
sc.pl.tracksplot(gcAnndata_germ, var_names=["Tfap2c", "Prdm14", "Pou5f1", "Nanog",
"Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2",
"Lefty1", "Lefty2", "Etv1", "Pitx2", "Hesx1", "Egr4",
"Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11",
"Atr", "Atm", "Msh6", "Aurka"],
gene_symbols = 'external_gene_name',
use_raw=False,
dendrogram=False,
groupby='stage_sex',
#save = "tracksplot_malefemale.pdf",
)
In [124]:
%%R -o Ezh2_derep_X
Ezh2_derep_X <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/CutNRun/QMUL_13Oct23/deepTools_OUT/H3K27me3_Female/DE_genes_in_Ezh2_dataset_chrX.txt", sep = "\t", header = F)
colnames(Ezh2_derep_X) = colnames = c("chromosome", "start", "end", "ensembl_gene_id", "external_gene_name")
In [125]:
sc.pl.matrixplot(gcAnndata_f,
var_names = gcAnndata_f.var[gcAnndata_f.var.geneid.isin(Ezh2_derep_X.ensembl_gene_id)].external_gene_name,
gene_symbols="external_gene_name",
cmap=heatcolors_wr,
groupby="stage_sex",
#dendrogram = True,
swap_axes = True,
standard_scale="var",
figsize = [3,8])#, save="_Ezh2_derep_X.pdf")
In [126]:
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex_celltype",
groups=["female_E11.5_somatic_cell"],
reference = "male_E11.5_somatic_cell",
method='wilcoxon',
use_raw = False,
tie_correct=True,
key_added="germ_soma",
pts=True
)
ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
finished: added to `.uns['germ_soma']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
In [127]:
germ_soma = sc.get.rank_genes_groups_df(gcAnndata, group="female_E11.5_somatic_cell", key="germ_soma", log2fc_min = 2, pval_cutoff=0.05)
germ_soma
# this is Xist
Out[127]:
| names | scores | logfoldchanges | pvals | pvals_adj | pct_nz_group | |
|---|---|---|---|---|---|---|
| 0 | ENSMUSG00000086503 | 6.074503 | 6.09966 | 1.243720e-09 | 0.000014 | 1.0 |
In [128]:
sc.tl.rank_genes_groups(gcAnndata_germ,
use_raw=False,
groupby="stage_sex",
groups=["female_E16.5", "female_E15.5", "female_E14.5", "female_E13.5", "female_E12.5", "female_E11.5", "female_E10.5", "female_E9.5"],
reference = 'rest',
method='wilcoxon',
tie_correct=True,
key_added="female",
pts=True)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:07)
In [129]:
sc.tl.rank_genes_groups(gcAnndata_germ,
groupby="stage_sex",
groups=["male_E16.5", "male_E15.5", "male_E14.5", "male_E13.5", "male_E12.5", "male_E11.5", "male_E10.5", "male_E9.5"],
reference = 'rest',
method='wilcoxon',
tie_correct=True,
key_added="male",
pts=True)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:08)
In [130]:
## Male E16.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E16.5"], reference = "male_E15.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E16.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E16_5[1:20]
## Male E15.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E15.5"], reference = "male_E14.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E15.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E15_5[1:20]
## Male E14.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E14.5"], reference = "male_E13.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E14.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E14_5[1:20]
## Male E13.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E13.5"], reference = "male_E12.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E13.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E13_5[1:20]
## Male E12.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E12.5"], reference = "male_E11.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E12.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E12_5[1:20]
## Male E11.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E11.5"], reference = "male_E10.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E11.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E11_5[1:20]
## Male E10.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E10.5"], reference = "male_E9.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E10_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E10.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E10_5[1:20]
ranking genes
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
Out[130]:
| names | scores | logfoldchanges | pvals | pvals_adj | pct_nz_group | |
|---|---|---|---|---|---|---|
| 1 | ENSMUSG00000053332 | 6.089863 | 1.514426 | 1.130071e-09 | 5.138761e-07 | 1.000000 |
| 2 | ENSMUSG00000040459 | 5.943998 | 2.714579 | 2.781523e-09 | 8.442897e-07 | 1.000000 |
| 3 | ENSMUSG00000074656 | 5.919688 | 1.746964 | 3.225538e-09 | 9.026130e-07 | 1.000000 |
| 4 | ENSMUSG00000071072 | 5.725201 | 1.613739 | 1.033114e-08 | 1.533999e-06 | 1.000000 |
| 5 | ENSMUSG00000090733 | 5.713046 | 1.502842 | 1.109720e-08 | 1.562700e-06 | 1.000000 |
| 6 | ENSMUSG00000114430 | 5.518103 | 8.323348 | 3.426794e-08 | 2.991866e-06 | 0.583333 |
| 7 | ENSMUSG00000009927 | 5.482092 | 1.591199 | 4.203241e-08 | 3.410563e-06 | 1.000000 |
| 8 | ENSMUSG00000057278 | 5.469937 | 1.505291 | 4.501951e-08 | 3.534679e-06 | 1.000000 |
| 9 | ENSMUSG00000060475 | 5.433471 | 1.959378 | 5.526829e-08 | 3.981323e-06 | 1.000000 |
| 10 | ENSMUSG00000038486 | 5.250492 | 24.453619 | 1.516937e-07 | 8.115243e-06 | 0.500000 |
| 11 | ENSMUSG00000037958 | 5.056823 | 2.336012 | 4.262994e-07 | 1.678038e-05 | 1.000000 |
| 12 | ENSMUSG00000029836 | 4.995876 | 1.754870 | 5.856930e-07 | 2.102619e-05 | 0.958333 |
| 13 | ENSMUSG00000066800 | 4.989806 | 23.183596 | 6.044012e-07 | 2.155599e-05 | 0.458333 |
| 14 | ENSMUSG00000075307 | 4.989806 | 26.324383 | 6.044012e-07 | 2.155599e-05 | 0.458333 |
| 15 | ENSMUSG00000022982 | 4.971565 | 1.552454 | 6.641455e-07 | 2.297354e-05 | 1.000000 |
| 16 | ENSMUSG00000014504 | 4.959410 | 1.902372 | 7.070770e-07 | 2.414575e-05 | 1.000000 |
| 17 | ENSMUSG00000078784 | 4.947254 | 2.059873 | 7.526762e-07 | 2.508193e-05 | 1.000000 |
| 18 | ENSMUSG00000039221 | 4.935099 | 1.609030 | 8.011016e-07 | 2.633380e-05 | 1.000000 |
| 19 | ENSMUSG00000113902 | 4.922944 | 1.954452 | 8.525209e-07 | 2.752659e-05 | 1.000000 |
In [131]:
## Female E16.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E15.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E16.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E16_5[1:20]
## Female E15.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E15.5"], reference = "female_E14.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E15.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E15_5[1:20]
## Female E14.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E14.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E14.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E14_5[1:20]
## Female E13.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E13.5"], reference = "female_E12.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E13.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E13_5[1:20]
## Female E12.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E12.5"], reference = "female_E11.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E12.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E12_5[1:20]
## Female E11.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E11.5"], reference = "female_E10.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E11.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E11_5[1:20]
## Male E11.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E10.5"], reference = "female_E9.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E10_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E10.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E10_5[1:20]
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
Out[131]:
| names | scores | logfoldchanges | pvals | pvals_adj | pct_nz_group | |
|---|---|---|---|---|---|---|
| 1 | ENSMUSG00000020180 | 7.369151 | 1.679681 | 1.717180e-13 | 7.496178e-10 | 1.000000 |
| 2 | ENSMUSG00000022114 | 7.107888 | 2.564145 | 1.178320e-12 | 2.857688e-09 | 1.000000 |
| 3 | ENSMUSG00000069309 | 6.657501 | 3.684765 | 2.785229e-11 | 1.676702e-08 | 1.000000 |
| 4 | ENSMUSG00000082766 | 6.527179 | 7.879493 | 6.702008e-11 | 2.985402e-08 | 0.680851 |
| 5 | ENSMUSG00000035851 | 6.339468 | 1.651302 | 2.305602e-10 | 7.293388e-08 | 1.000000 |
| 6 | ENSMUSG00000026878 | 6.316509 | 1.591184 | 2.675371e-10 | 8.342190e-08 | 0.978723 |
| 7 | ENSMUSG00000091625 | 6.285678 | 1.743592 | 3.264251e-10 | 9.374844e-08 | 1.000000 |
| 8 | ENSMUSG00000078784 | 6.185784 | 1.546489 | 6.179468e-10 | 1.466079e-07 | 0.978723 |
| 9 | ENSMUSG00000038486 | 5.951748 | 6.214230 | 2.652936e-09 | 4.420278e-07 | 0.617021 |
| 10 | ENSMUSG00000052748 | 5.855800 | 1.717659 | 4.747197e-09 | 6.452784e-07 | 0.978723 |
| 11 | ENSMUSG00000014504 | 5.855363 | 1.563164 | 4.759693e-09 | 6.452784e-07 | 0.978723 |
| 12 | ENSMUSG00000000365 | 5.830296 | 1.857088 | 5.532935e-09 | 7.188535e-07 | 0.978723 |
| 13 | ENSMUSG00000032643 | 5.614338 | 2.537704 | 1.973158e-08 | 1.897274e-06 | 0.957447 |
| 14 | ENSMUSG00000050199 | 5.513436 | 1.835604 | 3.518940e-08 | 3.012075e-06 | 0.978723 |
| 15 | ENSMUSG00000114430 | 5.459807 | 7.603449 | 4.766512e-08 | 3.755908e-06 | 0.595745 |
| 16 | ENSMUSG00000022781 | 5.383487 | 1.618103 | 7.305638e-08 | 5.351012e-06 | 0.978723 |
| 17 | ENSMUSG00000063253 | 5.288535 | 1.935931 | 1.233001e-07 | 8.332108e-06 | 0.936170 |
| 18 | ENSMUSG00000022197 | 5.164373 | 3.494497 | 2.412458e-07 | 1.379182e-05 | 0.723404 |
| 19 | ENSMUSG00000039209 | 5.002163 | 2.637339 | 5.669061e-07 | 2.616038e-05 | 0.744681 |
Male v Female gslist¶
In [132]:
sc.tl.rank_genes_groups(gcAnndata, 'stage_sex', method='wilcoxon', use_raw=False)
ranking genes
finished: added to `.uns['rank_genes_groups']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)
In [133]:
sc.pl.rank_genes_groups(gcAnndata, n_genes=25, sharey=False, gene_symbols="external_gene_name")
In [134]:
result = gcAnndata.uns['rank_genes_groups']
groups = result['names'].dtype.names
pd.DataFrame(
{group + '_' + key[:1]: result[key][group]
for group in groups for key in ['names', 'scores', 'logfoldchanges', 'pvals_adj']}).head(50)
Out[134]:
| female_E9.5_n | female_E9.5_s | female_E9.5_l | female_E9.5_p | female_E10.5_n | female_E10.5_s | female_E10.5_l | female_E10.5_p | female_E11.5_n | female_E11.5_s | female_E11.5_l | female_E11.5_p | female_E12.5_n | female_E12.5_s | female_E12.5_l | female_E12.5_p | female_E13.5_n | female_E13.5_s | female_E13.5_l | female_E13.5_p | female_E14.5_n | female_E14.5_s | female_E14.5_l | female_E14.5_p | female_E15.5_n | female_E15.5_s | female_E15.5_l | female_E15.5_p | female_E16.5_n | female_E16.5_s | female_E16.5_l | female_E16.5_p | male_E9.5_n | male_E9.5_s | male_E9.5_l | male_E9.5_p | male_E10.5_n | male_E10.5_s | male_E10.5_l | male_E10.5_p | male_E11.5_n | male_E11.5_s | male_E11.5_l | male_E11.5_p | male_E12.5_n | male_E12.5_s | male_E12.5_l | male_E12.5_p | male_E13.5_n | male_E13.5_s | male_E13.5_l | male_E13.5_p | male_E14.5_n | male_E14.5_s | male_E14.5_l | male_E14.5_p | male_E15.5_n | male_E15.5_s | male_E15.5_l | male_E15.5_p | male_E16.5_n | male_E16.5_s | male_E16.5_l | male_E16.5_p | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ENSMUSG00000025993 | 9.675285 | 3.506512 | 7.051691e-18 | ENSMUSG00000009927 | 10.553584 | 2.289993 | 6.425570e-22 | ENSMUSG00000020422 | 11.005273 | 3.124363 | 7.866966e-24 | ENSMUSG00000068048 | 10.203928 | 3.183214 | 4.156145e-20 | ENSMUSG00000015053 | 8.693368 | 2.885315 | 5.389155e-14 | ENSMUSG00000079184 | 10.776521 | 2.535967 | 9.699475e-23 | ENSMUSG00000029110 | 10.640263 | 2.477743 | 4.225319e-22 | ENSMUSG00000016427 | 13.642920 | 3.802901 | 2.427952e-38 | ENSMUSG00000039678 | 9.339453 | 2.834671 | 1.294557e-16 | ENSMUSG00000025362 | 7.666979 | 1.920825 | 2.640865e-10 | ENSMUSG00000060126 | 10.975850 | 1.714372 | 7.146918e-24 | ENSMUSG00000066652 | 10.123033 | 4.846027 | 9.531272e-20 | ENSMUSG00000046323 | 10.158194 | 2.625564 | 6.650036e-20 | ENSMUSG00000015656 | 11.121705 | 1.494284 | 2.147212e-24 | ENSMUSG00000017009 | 12.410044 | 3.629442 | 5.031710e-31 | ENSMUSG00000030720 | 13.479259 | 5.045060 | 3.275724e-37 |
| 1 | ENSMUSG00000070047 | 9.271043 | 2.799711 | 1.341154e-16 | ENSMUSG00000046330 | 10.513092 | 1.752015 | 6.425570e-22 | ENSMUSG00000022443 | 10.840289 | 2.456336 | 2.420277e-23 | ENSMUSG00000006200 | 10.006590 | 2.998120 | 1.556078e-19 | ENSMUSG00000002324 | 8.654793 | 4.295401 | 5.389155e-14 | ENSMUSG00000026239 | 10.708235 | 3.632599 | 1.016266e-22 | ENSMUSG00000005683 | 10.555483 | 2.258651 | 5.229435e-22 | ENSMUSG00000031397 | 13.155551 | 4.654744 | 1.150476e-35 | ENSMUSG00000050966 | 9.317941 | 2.721676 | 1.294557e-16 | ENSMUSG00000050621 | 7.574490 | 2.481963 | 2.640865e-10 | ENSMUSG00000068457 | 10.951319 | 3.280838 | 7.146918e-24 | ENSMUSG00000038793 | 9.452920 | 4.327497 | 2.905648e-17 | ENSMUSG00000040726 | 9.197942 | 3.419736 | 3.982022e-16 | ENSMUSG00000060985 | 10.904320 | 3.489329 | 7.996833e-24 | ENSMUSG00000064215 | 11.843365 | 2.978279 | 2.541931e-28 | ENSMUSG00000029223 | 13.394968 | 3.819025 | 4.363438e-37 |
| 2 | ENSMUSG00000005672 | 9.175815 | 2.649677 | 2.445568e-16 | ENSMUSG00000079641 | 10.497908 | 2.643158 | 6.425570e-22 | ENSMUSG00000051391 | 10.632948 | 2.119815 | 1.523426e-22 | ENSMUSG00000006585 | 9.358194 | 2.174949 | 5.901360e-17 | ENSMUSG00000070858 | 8.400472 | 1.808149 | 3.235416e-13 | ENSMUSG00000029848 | 10.583568 | 5.303363 | 1.938077e-22 | ENSMUSG00000005470 | 10.458048 | 3.059304 | 9.793018e-22 | ENSMUSG00000027855 | 12.862712 | 3.865922 | 2.654570e-34 | ENSMUSG00000025993 | 9.178117 | 3.227013 | 2.393861e-16 | ENSMUSG00000090733 | 7.573617 | 2.238895 | 2.640865e-10 | ENSMUSG00000020402 | 10.336987 | 2.102242 | 3.487750e-21 | ENSMUSG00000054405 | 9.432779 | 2.014945 | 2.905648e-17 | ENSMUSG00000000579 | 9.014544 | 1.386154 | 1.438324e-15 | ENSMUSG00000006273 | 10.290380 | 2.412115 | 2.832716e-21 | ENSMUSG00000058550 | 11.744291 | 2.830286 | 5.496978e-28 | ENSMUSG00000028784 | 13.372955 | 6.049841 | 4.363438e-37 |
| 3 | ENSMUSG00000020372 | 9.093369 | 1.251233 | 4.191537e-16 | ENSMUSG00000047675 | 10.284691 | 2.171756 | 2.968842e-21 | ENSMUSG00000031386 | 10.483914 | 1.943355 | 5.588559e-22 | ENSMUSG00000034708 | 9.262971 | 2.367940 | 8.937772e-17 | ENSMUSG00000029848 | 8.314445 | 4.209333 | 5.030145e-13 | ENSMUSG00000022432 | 10.550991 | 4.120283 | 1.952663e-22 | ENSMUSG00000085601 | 10.363777 | 4.782912 | 1.977321e-21 | ENSMUSG00000078938 | 12.790547 | 4.348732 | 5.051986e-34 | ENSMUSG00000028640 | 9.147747 | 2.451515 | 2.537739e-16 | ENSMUSG00000047675 | 7.524755 | 2.484592 | 2.882207e-10 | ENSMUSG00000059005 | 10.126877 | 2.002101 | 2.290985e-20 | ENSMUSG00000063856 | 9.221128 | 2.376962 | 1.604226e-16 | ENSMUSG00000024521 | 8.907993 | 3.335130 | 2.835564e-15 | ENSMUSG00000062382 | 10.274092 | 1.419304 | 2.875193e-21 | ENSMUSG00000076617 | 11.666992 | 3.978752 | 1.025600e-27 | ENSMUSG00000000730 | 13.361144 | 5.864875 | 4.363438e-37 |
| 4 | ENSMUSG00000078193 | 9.026900 | 1.157818 | 6.424539e-16 | ENSMUSG00000086290 | 10.277099 | 3.083502 | 2.968842e-21 | ENSMUSG00000015023 | 9.752338 | 2.159153 | 7.158992e-19 | ENSMUSG00000005566 | 9.259839 | 2.052994 | 8.937772e-17 | ENSMUSG00000062683 | 7.164023 | 0.858845 | 3.419982e-09 | ENSMUSG00000051455 | 10.532824 | 4.049201 | 1.952663e-22 | ENSMUSG00000024841 | 10.309999 | 2.448106 | 2.772121e-21 | ENSMUSG00000031432 | 12.599156 | 5.854750 | 3.583250e-33 | ENSMUSG00000070047 | 9.027536 | 2.639028 | 6.387309e-16 | ENSMUSG00000060636 | 7.438374 | 1.920056 | 4.246982e-10 | ENSMUSG00000031523 | 9.925300 | 2.433719 | 1.410597e-19 | ENSMUSG00000030246 | 9.075022 | 2.691470 | 4.961495e-16 | ENSMUSG00000030246 | 8.584466 | 2.479161 | 3.511602e-14 | ENSMUSG00000000902 | 10.227734 | 1.715963 | 4.064325e-21 | ENSMUSG00000052374 | 11.327856 | 3.041988 | 4.169558e-26 | ENSMUSG00000020265 | 13.283297 | 2.420750 | 9.022987e-37 |
| 5 | ENSMUSG00000030539 | 8.804487 | 2.545866 | 4.098870e-15 | ENSMUSG00000022114 | 10.276466 | 3.153666 | 2.968842e-21 | ENSMUSG00000024949 | 9.728806 | 1.383166 | 7.158992e-19 | ENSMUSG00000095180 | 8.953495 | 2.261455 | 1.252871e-15 | ENSMUSG00000031158 | 6.938035 | 1.707143 | 1.239741e-08 | ENSMUSG00000027879 | 10.530318 | 2.893320 | 1.952663e-22 | ENSMUSG00000022837 | 10.272038 | 3.318377 | 3.426621e-21 | ENSMUSG00000096210 | 12.522547 | 4.370077 | 8.592425e-33 | ENSMUSG00000021938 | 8.991473 | 1.718026 | 7.606361e-16 | ENSMUSG00000053332 | 7.414816 | 2.510639 | 4.246982e-10 | ENSMUSG00000022048 | 9.797847 | 2.392932 | 4.183906e-19 | ENSMUSG00000005732 | 9.036788 | 1.686583 | 5.869387e-16 | ENSMUSG00000096255 | 8.578009 | 1.428677 | 3.511602e-14 | ENSMUSG00000052374 | 10.132510 | 3.135312 | 9.612178e-21 | ENSMUSG00000000579 | 11.205919 | 1.560410 | 1.387367e-25 | ENSMUSG00000031765 | 13.253231 | 4.543951 | 1.179177e-36 |
| 6 | ENSMUSG00000020257 | 8.785953 | 2.077950 | 4.230204e-15 | ENSMUSG00000062328 | 10.267609 | 1.556812 | 2.968842e-21 | ENSMUSG00000006498 | 9.727760 | 1.708000 | 7.158992e-19 | ENSMUSG00000015176 | 8.847936 | 1.630474 | 2.779926e-15 | ENSMUSG00000031146 | 6.799439 | 1.394861 | 2.547150e-08 | ENSMUSG00000085601 | 10.498368 | 4.777515 | 2.397870e-22 | ENSMUSG00000067702 | 10.237240 | 3.304119 | 4.210562e-21 | ENSMUSG00000020059 | 12.518625 | 3.704074 | 8.592425e-33 | ENSMUSG00000081683 | 8.955410 | 2.592590 | 9.234905e-16 | ENSMUSG00000067288 | 7.350248 | 2.200550 | 5.397799e-10 | ENSMUSG00000046434 | 9.758918 | 1.859327 | 5.268045e-19 | ENSMUSG00000016921 | 8.873612 | 1.467029 | 2.207980e-15 | ENSMUSG00000063856 | 8.505037 | 2.090800 | 5.660610e-14 | ENSMUSG00000058550 | 10.121861 | 2.819745 | 9.646129e-21 | ENSMUSG00000051316 | 11.117189 | 2.012710 | 3.226688e-25 | ENSMUSG00000067338 | 13.120084 | 3.900148 | 5.795057e-36 |
| 7 | ENSMUSG00000050966 | 8.717567 | 2.552437 | 6.893533e-15 | ENSMUSG00000034892 | 10.248628 | 2.115961 | 2.968842e-21 | ENSMUSG00000035621 | 9.588923 | 2.231044 | 2.428498e-18 | ENSMUSG00000004264 | 8.711679 | 1.316128 | 8.168981e-15 | ENSMUSG00000022982 | 6.701124 | 1.090657 | 3.761935e-08 | ENSMUSG00000009549 | 10.439480 | 2.180382 | 3.970213e-22 | ENSMUSG00000021276 | 10.215096 | 2.184067 | 4.630386e-21 | ENSMUSG00000051592 | 12.486465 | 4.781847 | 1.207292e-32 | ENSMUSG00000027597 | 8.888977 | 1.826851 | 1.346158e-15 | ENSMUSG00000025290 | 7.326690 | 1.644644 | 5.721278e-10 | ENSMUSG00000069045 | 9.663995 | 2.637060 | 1.169947e-18 | ENSMUSG00000055044 | 8.439387 | 2.954699 | 8.703633e-14 | ENSMUSG00000041359 | 8.476624 | 2.286723 | 6.325031e-14 | ENSMUSG00000024312 | 10.049816 | 1.978088 | 1.826304e-20 | ENSMUSG00000056155 | 11.077995 | 3.290473 | 4.376982e-25 | ENSMUSG00000040013 | 13.116325 | 2.764854 | 5.795057e-36 |
| 8 | ENSMUSG00000028640 | 8.644069 | 2.261034 | 1.184027e-14 | ENSMUSG00000057322 | 10.246730 | 2.125862 | 2.968842e-21 | ENSMUSG00000022463 | 9.520420 | 1.790144 | 4.183043e-18 | ENSMUSG00000001270 | 8.385288 | 2.722079 | 1.227204e-13 | ENSMUSG00000021361 | 6.578230 | 1.272332 | 7.422451e-08 | ENSMUSG00000051159 | 10.386230 | 3.689216 | 5.797064e-22 | ENSMUSG00000028955 | 10.201809 | 2.687172 | 4.719814e-21 | ENSMUSG00000005883 | 12.137934 | 3.430297 | 6.910178e-31 | ENSMUSG00000078193 | 8.861139 | 1.165304 | 1.571566e-15 | ENSMUSG00000041453 | 7.282190 | 1.754996 | 6.663122e-10 | ENSMUSG00000002265 | 9.607468 | 2.474684 | 1.803270e-18 | ENSMUSG00000026520 | 8.415492 | 2.413517 | 9.488688e-14 | ENSMUSG00000029447 | 8.410110 | 1.212361 | 9.934387e-14 | ENSMUSG00000037058 | 10.003457 | 1.438324 | 2.676849e-20 | ENSMUSG00000038845 | 11.005595 | 1.506394 | 8.709848e-25 | ENSMUSG00000059625 | 13.014854 | 5.356165 | 1.998837e-35 |
| 9 | ENSMUSG00000034165 | 8.601248 | 1.857363 | 1.564681e-14 | ENSMUSG00000025362 | 10.110069 | 1.593437 | 1.088060e-20 | ENSMUSG00000025278 | 9.403545 | 2.218333 | 1.046798e-17 | ENSMUSG00000051695 | 8.318882 | 1.869896 | 1.823702e-13 | ENSMUSG00000015217 | 6.561844 | 1.122764 | 7.733503e-08 | ENSMUSG00000028618 | 10.384351 | 2.941988 | 5.797064e-22 | ENSMUSG00000079507 | 10.163531 | 3.717565 | 6.295709e-21 | ENSMUSG00000071103 | 12.028642 | 4.128081 | 2.285098e-30 | ENSMUSG00000005672 | 8.780787 | 2.482570 | 2.952727e-15 | ENSMUSG00000071072 | 7.247289 | 2.443662 | 6.663122e-10 | ENSMUSG00000051391 | 9.453352 | 1.825916 | 7.162547e-18 | ENSMUSG00000016344 | 8.365651 | 1.624217 | 1.304898e-13 | ENSMUSG00000032346 | 8.271593 | 2.783371 | 2.885108e-13 | ENSMUSG00000028378 | 9.963364 | 2.873443 | 3.701773e-20 | ENSMUSG00000044595 | 10.982188 | 2.406122 | 1.016071e-24 | ENSMUSG00000060743 | 12.981567 | 1.988274 | 2.831350e-35 |
| 10 | ENSMUSG00000031169 | 8.540531 | 2.480062 | 2.430180e-14 | ENSMUSG00000020180 | 10.041105 | 2.036696 | 1.995005e-20 | ENSMUSG00000036617 | 9.304712 | 2.919814 | 2.392999e-17 | ENSMUSG00000030605 | 8.305099 | 1.721778 | 1.823702e-13 | ENSMUSG00000057762 | 6.403448 | 1.297241 | 2.072295e-07 | ENSMUSG00000030041 | 10.332980 | 4.657975 | 9.091505e-22 | ENSMUSG00000030041 | 10.127151 | 4.817700 | 7.547670e-21 | ENSMUSG00000000563 | 12.012431 | 2.280046 | 2.669202e-30 | ENSMUSG00000019989 | 8.713089 | 2.347644 | 4.964856e-15 | ENSMUSG00000034892 | 7.246416 | 2.386563 | 6.663122e-10 | ENSMUSG00000032249 | 9.426688 | 1.706003 | 8.398271e-18 | ENSMUSG00000004864 | 8.294646 | 2.318275 | 2.038660e-13 | ENSMUSG00000025393 | 8.173114 | 0.982696 | 5.963324e-13 | ENSMUSG00000039756 | 9.927654 | 1.627596 | 4.359673e-20 | ENSMUSG00000028964 | 10.956603 | 1.205996 | 1.225782e-24 | ENSMUSG00000004032 | 12.935396 | 3.435295 | 4.430194e-35 |
| 11 | ENSMUSG00000063229 | 8.520719 | 1.935022 | 2.662324e-14 | ENSMUSG00000090862 | 10.025921 | 2.058720 | 2.099006e-20 | ENSMUSG00000002265 | 9.298437 | 2.550425 | 2.392999e-17 | ENSMUSG00000060860 | 8.304474 | 1.568107 | 1.823702e-13 | ENSMUSG00000002010 | 6.360435 | 1.100290 | 2.583075e-07 | ENSMUSG00000000346 | 10.321704 | 3.197427 | 9.438771e-22 | ENSMUSG00000051159 | 10.122723 | 3.529925 | 7.547670e-21 | ENSMUSG00000009596 | 11.981579 | 3.759052 | 3.725632e-30 | ENSMUSG00000071235 | 8.692210 | 2.467097 | 5.288858e-15 | ENSMUSG00000059291 | 7.242054 | 1.749781 | 6.663122e-10 | ENSMUSG00000021840 | 9.409623 | 1.474293 | 9.056714e-18 | ENSMUSG00000009013 | 8.291232 | 1.059263 | 2.038660e-13 | ENSMUSG00000027404 | 7.894790 | 1.046342 | 5.289501e-12 | ENSMUSG00000022234 | 9.926402 | 1.220191 | 4.359673e-20 | ENSMUSG00000026354 | 10.699393 | 3.614129 | 1.863430e-23 | ENSMUSG00000021681 | 12.848689 | 3.183676 | 1.265890e-34 |
| 12 | ENSMUSG00000039678 | 8.486526 | 2.667356 | 3.282079e-14 | ENSMUSG00000041841 | 10.019594 | 1.709529 | 2.099006e-20 | ENSMUSG00000040661 | 9.267846 | 1.957644 | 2.961350e-17 | ENSMUSG00000018677 | 8.214262 | 1.428880 | 3.584215e-13 | ENSMUSG00000079435 | 6.299671 | 0.963320 | 3.554708e-07 | ENSMUSG00000025480 | 10.280356 | 3.648048 | 1.347149e-21 | ENSMUSG00000058076 | 10.120192 | 2.276411 | 7.547670e-21 | ENSMUSG00000045193 | 11.940790 | 2.441214 | 5.863443e-30 | ENSMUSG00000034165 | 8.689680 | 1.838443 | 5.288858e-15 | ENSMUSG00000058546 | 7.238564 | 1.563402 | 6.663122e-10 | ENSMUSG00000039202 | 9.393092 | 2.016654 | 9.782577e-18 | ENSMUSG00000078812 | 8.264606 | 0.984336 | 2.353244e-13 | ENSMUSG00000059796 | 7.874125 | 1.199098 | 5.739314e-12 | ENSMUSG00000024966 | 9.882549 | 1.388733 | 6.046914e-20 | ENSMUSG00000024767 | 10.667004 | 1.594718 | 2.438491e-23 | ENSMUSG00000020799 | 12.844126 | 5.489536 | 1.265890e-34 |
| 13 | ENSMUSG00000026701 | 8.479815 | 2.124991 | 3.282079e-14 | ENSMUSG00000063511 | 9.978469 | 2.008853 | 2.844759e-20 | ENSMUSG00000048154 | 9.238300 | 1.852573 | 3.644287e-17 | ENSMUSG00000047945 | 8.204239 | 1.440769 | 3.617937e-13 | ENSMUSG00000022450 | 6.292161 | 0.923928 | 3.554708e-07 | ENSMUSG00000042682 | 10.232119 | 2.369933 | 1.922453e-21 | ENSMUSG00000009291 | 10.102476 | 2.698932 | 8.397850e-21 | ENSMUSG00000045106 | 11.736325 | 3.262661 | 5.329347e-29 | ENSMUSG00000026475 | 8.630207 | 2.609254 | 8.354422e-15 | ENSMUSG00000063457 | 7.228093 | 1.895178 | 6.663122e-10 | ENSMUSG00000025278 | 9.385093 | 2.201102 | 9.800496e-18 | ENSMUSG00000028945 | 8.252999 | 1.552269 | 2.366319e-13 | ENSMUSG00000035557 | 7.858627 | 3.381784 | 5.739314e-12 | ENSMUSG00000050708 | 9.881923 | 1.407021 | 6.046914e-20 | ENSMUSG00000028378 | 10.367062 | 2.726956 | 5.458732e-22 | ENSMUSG00000024164 | 12.821845 | 5.539739 | 1.452046e-34 |
| 14 | ENSMUSG00000003153 | 8.471507 | 2.583922 | 3.304594e-14 | ENSMUSG00000060636 | 9.975306 | 1.495595 | 2.844759e-20 | ENSMUSG00000066232 | 9.219998 | 1.863410 | 4.053082e-17 | ENSMUSG00000054405 | 8.092727 | 1.359733 | 8.489766e-13 | ENSMUSG00000048450 | 6.219790 | 2.874785 | 4.519880e-07 | ENSMUSG00000025066 | 10.225227 | 1.620489 | 1.922453e-21 | ENSMUSG00000030760 | 10.071791 | 3.381085 | 1.071341e-20 | ENSMUSG00000020774 | 11.713317 | 4.867268 | 6.793391e-29 | ENSMUSG00000020782 | 8.599838 | 2.398123 | 9.895918e-15 | ENSMUSG00000049517 | 7.222858 | 1.704399 | 6.663122e-10 | ENSMUSG00000071172 | 9.250175 | 1.145529 | 3.261344e-17 | ENSMUSG00000003873 | 8.246854 | 1.442206 | 2.366319e-13 | ENSMUSG00000009013 | 7.856689 | 1.017823 | 5.739314e-12 | ENSMUSG00000005312 | 9.848093 | 2.068751 | 8.025063e-20 | ENSMUSG00000060376 | 10.345286 | 1.825553 | 6.396738e-22 | ENSMUSG00000038393 | 12.818892 | 3.154333 | 1.452046e-34 |
| 15 | ENSMUSG00000035202 | 8.458085 | 1.889859 | 3.489795e-14 | ENSMUSG00000067288 | 9.937977 | 1.750292 | 3.881748e-20 | ENSMUSG00000026193 | 9.209278 | 2.172497 | 4.215514e-17 | ENSMUSG00000010760 | 8.044489 | 2.428791 | 1.181471e-12 | ENSMUSG00000010592 | 6.217741 | 1.813606 | 4.519880e-07 | ENSMUSG00000047260 | 10.218336 | 1.998312 | 1.922453e-21 | ENSMUSG00000029848 | 10.041739 | 4.620284 | 1.362796e-20 | ENSMUSG00000026864 | 11.689785 | 1.932202 | 8.715647e-29 | ENSMUSG00000027547 | 8.597307 | 2.050077 | 9.895918e-15 | ENSMUSG00000009927 | 7.220240 | 2.737736 | 6.663122e-10 | ENSMUSG00000066232 | 9.237376 | 1.884769 | 3.446165e-17 | ENSMUSG00000034681 | 8.158097 | 1.064744 | 4.642911e-13 | ENSMUSG00000017009 | 7.810841 | 2.454726 | 7.749663e-12 | ENSMUSG00000035960 | 9.828672 | 1.477400 | 9.246805e-20 | ENSMUSG00000074397 | 10.317525 | 2.161304 | 8.010176e-22 | ENSMUSG00000000739 | 12.748829 | 5.672569 | 3.139751e-34 |
| 16 | ENSMUSG00000070327 | 8.425810 | 2.346476 | 4.115695e-14 | ENSMUSG00000061983 | 9.917731 | 2.684566 | 4.475692e-20 | ENSMUSG00000029094 | 9.202742 | 2.344470 | 4.231190e-17 | ENSMUSG00000020358 | 7.957410 | 1.712948 | 2.255607e-12 | ENSMUSG00000041765 | 6.213645 | 1.453722 | 4.519880e-07 | ENSMUSG00000029148 | 10.216457 | 2.618429 | 1.922453e-21 | ENSMUSG00000040652 | 10.012002 | 2.122159 | 1.733229e-20 | ENSMUSG00000020664 | 11.684556 | 2.292654 | 9.018495e-29 | ENSMUSG00000005360 | 8.589082 | 2.282080 | 1.007107e-14 | ENSMUSG00000008683 | 7.189702 | 1.909107 | 7.873872e-10 | ENSMUSG00000026421 | 9.103257 | 2.130830 | 1.125534e-16 | ENSMUSG00000018293 | 8.117133 | 1.487397 | 6.128581e-13 | ENSMUSG00000022234 | 7.744973 | 0.872920 | 1.227481e-11 | ENSMUSG00000064215 | 9.802361 | 2.772730 | 1.063357e-19 | ENSMUSG00000041881 | 10.231516 | 1.157656 | 1.839369e-21 | ENSMUSG00000016253 | 12.739970 | 2.557996 | 3.364487e-34 |
| 17 | ENSMUSG00000067860 | 8.364774 | 2.241654 | 5.975660e-14 | ENSMUSG00000093674 | 9.895587 | 1.917257 | 5.275476e-20 | ENSMUSG00000091405 | 9.176072 | 2.721152 | 5.136276e-17 | ENSMUSG00000020733 | 7.919821 | 1.857206 | 2.884218e-12 | ENSMUSG00000096472 | 6.199307 | 2.272567 | 4.760982e-07 | ENSMUSG00000008892 | 10.194530 | 1.830856 | 2.289167e-21 | ENSMUSG00000000346 | 9.981000 | 3.043229 | 2.147846e-20 | ENSMUSG00000112858 | 11.660239 | 3.089995 | 1.168716e-28 | ENSMUSG00000031995 | 8.530242 | 2.487493 | 1.517931e-14 | ENSMUSG00000079641 | 7.173123 | 2.744446 | 8.421299e-10 | ENSMUSG00000027878 | 9.091259 | 2.118546 | 1.187135e-16 | ENSMUSG00000060461 | 7.983315 | 2.251044 | 1.727418e-12 | ENSMUSG00000060461 | 7.659086 | 2.121009 | 2.270750e-11 | ENSMUSG00000068039 | 9.800482 | 1.115191 | 1.063357e-19 | ENSMUSG00000031781 | 10.098692 | 1.472313 | 6.788683e-21 | ENSMUSG00000011589 | 12.721179 | 4.018242 | 4.019609e-34 |
| 18 | ENSMUSG00000070780 | 8.344322 | 2.300981 | 6.797062e-14 | ENSMUSG00000071415 | 9.864585 | 1.624612 | 6.809910e-20 | ENSMUSG00000070047 | 9.163522 | 2.196258 | 5.323093e-17 | ENSMUSG00000030057 | 7.865319 | 1.018877 | 4.229372e-12 | ENSMUSG00000063714 | 6.146054 | 1.457917 | 6.421541e-07 | ENSMUSG00000011349 | 10.173857 | 4.094938 | 2.696441e-21 | ENSMUSG00000046338 | 9.967713 | 3.236759 | 2.293390e-20 | ENSMUSG00000112920 | 11.495779 | 4.146809 | 6.871828e-28 | ENSMUSG00000048087 | 8.465708 | 1.645417 | 2.525999e-14 | ENSMUSG00000038900 | 7.165270 | 2.115061 | 8.472500e-10 | ENSMUSG00000040661 | 9.062729 | 1.890641 | 1.461576e-16 | ENSMUSG00000021377 | 7.931427 | 1.727744 | 2.488763e-12 | ENSMUSG00000067148 | 7.646816 | 1.315973 | 2.366693e-11 | ENSMUSG00000076617 | 9.771664 | 3.496658 | 1.354955e-19 | ENSMUSG00000037772 | 10.064941 | 1.531571 | 9.067885e-21 | ENSMUSG00000025372 | 12.719569 | 3.497971 | 4.019609e-34 |
| 19 | ENSMUSG00000074637 | 8.305975 | 1.968261 | 9.003900e-14 | ENSMUSG00000025508 | 9.836114 | 1.719935 | 8.588026e-20 | ENSMUSG00000032249 | 9.161430 | 1.748122 | 5.323093e-17 | ENSMUSG00000034681 | 7.792648 | 0.928247 | 6.706913e-12 | ENSMUSG00000022884 | 6.093482 | 1.319857 | 8.612351e-07 | ENSMUSG00000003824 | 10.145666 | 2.342198 | 3.436836e-21 | ENSMUSG00000011349 | 9.963284 | 4.276134 | 2.293390e-20 | ENSMUSG00000015365 | 11.460743 | 2.944228 | 9.856589e-28 | ENSMUSG00000059288 | 8.435655 | 2.165314 | 3.052583e-14 | ENSMUSG00000041841 | 7.146947 | 1.880954 | 8.859246e-10 | ENSMUSG00000024590 | 9.029665 | 2.010623 | 1.879276e-16 | ENSMUSG00000028851 | 7.917089 | 1.261961 | 2.653457e-12 | ENSMUSG00000047751 | 7.637775 | 1.748506 | 2.411955e-11 | ENSMUSG00000025613 | 9.736582 | 1.347613 | 1.837981e-19 | ENSMUSG00000096255 | 10.052422 | 1.493729 | 9.782591e-21 | ENSMUSG00000054932 | 12.694604 | 4.254623 | 5.318218e-34 |
| 20 | ENSMUSG00000027597 | 8.298944 | 1.738588 | 9.170836e-14 | ENSMUSG00000012848 | 9.808908 | 1.425149 | 1.071368e-19 | ENSMUSG00000031523 | 9.155678 | 2.242032 | 5.359283e-17 | ENSMUSG00000070837 | 7.741904 | 2.773221 | 9.174379e-12 | ENSMUSG00000007872 | 6.015650 | 1.914248 | 1.303557e-06 | ENSMUSG00000024099 | 10.134390 | 2.174072 | 3.689660e-21 | ENSMUSG00000020097 | 9.941773 | 2.648637 | 2.541836e-20 | ENSMUSG00000025090 | 11.404789 | 2.966070 | 1.837804e-27 | ENSMUSG00000070780 | 8.433440 | 2.317370 | 3.052583e-14 | ENSMUSG00000093674 | 7.139967 | 2.213113 | 8.859246e-10 | ENSMUSG00000035530 | 9.006468 | 0.932917 | 2.211797e-16 | ENSMUSG00000026305 | 7.905483 | 2.512095 | 2.774082e-12 | ENSMUSG00000025491 | 7.575782 | 1.895103 | 3.541630e-11 | ENSMUSG00000002524 | 9.695234 | 1.784542 | 2.652068e-19 | ENSMUSG00000021037 | 9.962602 | 1.354744 | 2.309202e-20 | ENSMUSG00000027510 | 12.673397 | 2.866393 | 6.712946e-34 |
| 21 | ENSMUSG00000054091 | 8.252929 | 1.297691 | 1.257517e-13 | ENSMUSG00000061787 | 9.738048 | 1.885617 | 2.044133e-19 | ENSMUSG00000031691 | 9.143389 | 2.174271 | 5.544597e-17 | ENSMUSG00000024827 | 7.738145 | 2.777019 | 9.174379e-12 | ENSMUSG00000062054 | 5.947375 | 1.306615 | 1.802223e-06 | ENSMUSG00000032065 | 10.099934 | 3.157649 | 4.760814e-21 | ENSMUSG00000031158 | 9.932283 | 2.365777 | 2.541836e-20 | ENSMUSG00000031431 | 11.391716 | 3.475548 | 2.091035e-27 | ENSMUSG00000037013 | 8.425216 | 2.001610 | 3.143846e-14 | ENSMUSG00000054766 | 7.084997 | 2.633518 | 1.264578e-09 | ENSMUSG00000020186 | 8.938743 | 1.856368 | 3.905296e-16 | ENSMUSG00000061104 | 7.819457 | 1.235613 | 5.263451e-12 | ENSMUSG00000039329 | 7.506686 | 1.750091 | 5.597424e-11 | ENSMUSG00000019139 | 9.664537 | 1.985692 | 3.448191e-19 | ENSMUSG00000060985 | 9.949537 | 2.397631 | 2.513664e-20 | ENSMUSG00000048997 | 12.668565 | 4.358173 | 6.884500e-34 |
| 22 | ENSMUSG00000052609 | 8.248455 | 2.208777 | 1.257517e-13 | ENSMUSG00000057113 | 9.734251 | 1.819364 | 2.044133e-19 | ENSMUSG00000046434 | 9.142605 | 1.722893 | 5.544597e-17 | ENSMUSG00000037169 | 7.719978 | 1.880798 | 1.015828e-11 | ENSMUSG00000022914 | 5.923138 | 1.339969 | 2.027700e-06 | ENSMUSG00000057054 | 10.097427 | 4.349154 | 4.760814e-21 | ENSMUSG00000022404 | 9.926589 | 2.894901 | 2.541836e-20 | ENSMUSG00000060445 | 11.356419 | 2.942189 | 2.949304e-27 | ENSMUSG00000028766 | 8.387887 | 2.034470 | 4.099324e-14 | ENSMUSG00000040459 | 7.036135 | 2.753390 | 1.659235e-09 | ENSMUSG00000035203 | 8.932343 | 2.009212 | 3.958092e-16 | ENSMUSG00000045394 | 7.787368 | 1.891863 | 6.493005e-12 | ENSMUSG00000021273 | 7.504748 | 1.541148 | 5.597424e-11 | ENSMUSG00000037958 | 9.583097 | 1.847547 | 6.631038e-19 | ENSMUSG00000005413 | 9.925313 | 2.877556 | 3.066094e-20 | ENSMUSG00000020661 | 12.647626 | 3.678220 | 8.678565e-34 |
| 23 | ENSMUSG00000020649 | 8.238229 | 2.123487 | 1.285287e-13 | ENSMUSG00000037563 | 9.712740 | 1.252711 | 2.419954e-19 | ENSMUSG00000060206 | 9.129532 | 2.132005 | 6.006475e-17 | ENSMUSG00000005732 | 7.707448 | 1.293624 | 1.077566e-11 | ENSMUSG00000001018 | 5.816630 | 1.378490 | 3.640613e-06 | ENSMUSG00000052525 | 10.076128 | 3.739043 | 5.695055e-21 | ENSMUSG00000039231 | 9.923426 | 2.583941 | 2.541836e-20 | ENSMUSG00000030309 | 11.256539 | 3.022508 | 8.234447e-27 | ENSMUSG00000021464 | 8.385039 | 2.382290 | 4.099324e-14 | ENSMUSG00000108414 | 7.023920 | 2.398438 | 1.743972e-09 | ENSMUSG00000074578 | 8.915278 | 1.716906 | 4.415067e-16 | ENSMUSG00000006442 | 7.774396 | 1.732622 | 6.894448e-12 | ENSMUSG00000026641 | 7.483115 | 1.918973 | 6.168533e-11 | ENSMUSG00000041438 | 9.526088 | 1.925068 | 1.080251e-18 | ENSMUSG00000062382 | 9.913065 | 1.096484 | 3.321934e-20 | ENSMUSG00000036912 | 12.639036 | 4.858409 | 9.358019e-34 |
| 24 | ENSMUSG00000021255 | 8.236951 | 2.166359 | 1.285287e-13 | ENSMUSG00000020460 | 9.676676 | 1.311521 | 3.307507e-19 | ENSMUSG00000022048 | 9.114367 | 2.118188 | 6.643078e-17 | ENSMUSG00000033751 | 7.627887 | 1.336339 | 1.929125e-11 | ENSMUSG00000028843 | 5.793758 | 1.316504 | 3.831754e-06 | ENSMUSG00000002076 | 10.026011 | 2.939042 | 8.817744e-21 | ENSMUSG00000027879 | 9.922792 | 2.716861 | 2.541836e-20 | ENSMUSG00000039217 | 11.214967 | 3.770557 | 1.273794e-26 | ENSMUSG00000016319 | 8.376498 | 2.116309 | 4.250469e-14 | ENSMUSG00000039001 | 7.000361 | 1.926194 | 1.990186e-09 | ENSMUSG00000089774 | 8.911012 | 1.891867 | 4.415067e-16 | ENSMUSG00000036438 | 7.743672 | 1.141142 | 8.432697e-12 | ENSMUSG00000066705 | 7.481501 | 2.452103 | 6.168533e-11 | ENSMUSG00000031880 | 9.499777 | 3.859593 | 1.350331e-18 | ENSMUSG00000067764 | 9.868427 | 3.076215 | 4.981089e-20 | ENSMUSG00000018012 | 12.612729 | 4.476464 | 1.225466e-33 |
| 25 | ENSMUSG00000031995 | 8.217138 | 2.351220 | 1.467461e-13 | ENSMUSG00000063316 | 9.668451 | 1.439178 | 3.423025e-19 | ENSMUSG00000035203 | 9.101294 | 2.193334 | 7.216039e-17 | ENSMUSG00000010660 | 7.618490 | 2.258573 | 2.000761e-11 | ENSMUSG00000030605 | 5.790344 | 1.424774 | 3.831754e-06 | ENSMUSG00000030086 | 10.008470 | 2.726532 | 1.017869e-20 | ENSMUSG00000061232 | 9.922476 | 4.666304 | 2.541836e-20 | ENSMUSG00000028109 | 11.208169 | 2.533911 | 1.352570e-26 | ENSMUSG00000010660 | 8.361946 | 2.426985 | 4.643266e-14 | ENSMUSG00000028936 | 6.961097 | 1.984537 | 2.541218e-09 | ENSMUSG00000035621 | 8.862217 | 2.697753 | 6.417325e-16 | ENSMUSG00000004264 | 7.725921 | 1.235857 | 9.322425e-12 | ENSMUSG00000047407 | 7.458253 | 2.308014 | 7.087879e-11 | ENSMUSG00000005656 | 9.493512 | 1.707079 | 1.393058e-18 | ENSMUSG00000031590 | 9.842299 | 1.611926 | 6.212358e-20 | ENSMUSG00000004937 | 12.607897 | 2.150565 | 1.263468e-33 |
| 26 | ENSMUSG00000063445 | 8.202438 | 1.974003 | 1.606740e-13 | ENSMUSG00000090733 | 9.665288 | 1.592464 | 3.423025e-19 | ENSMUSG00000031167 | 9.085867 | 1.238898 | 8.019508e-17 | ENSMUSG00000016921 | 7.584661 | 1.014088 | 2.509041e-11 | ENSMUSG00000054717 | 5.778738 | 1.275893 | 3.911353e-06 | ENSMUSG00000061232 | 9.995314 | 4.525886 | 1.089857e-20 | ENSMUSG00000022940 | 9.921527 | 2.774864 | 2.541836e-20 | ENSMUSG00000021114 | 11.151954 | 2.227754 | 2.426988e-26 | ENSMUSG00000067878 | 8.327148 | 2.419141 | 6.025182e-14 | ENSMUSG00000045128 | 6.949754 | 1.402465 | 2.662343e-09 | ENSMUSG00000029581 | 8.860884 | 2.104371 | 6.417325e-16 | ENSMUSG00000054766 | 7.667888 | 1.942161 | 1.413512e-11 | ENSMUSG00000095677 | 7.440817 | 1.270471 | 7.605369e-11 | ENSMUSG00000033628 | 9.482862 | 2.427002 | 1.500020e-18 | ENSMUSG00000021178 | 9.824879 | 1.394413 | 7.112353e-20 | ENSMUSG00000038080 | 12.595549 | 4.368733 | 1.431629e-33 |
| 27 | ENSMUSG00000021464 | 8.155144 | 2.219463 | 2.238973e-13 | ENSMUSG00000030432 | 9.654532 | 1.688060 | 3.666168e-19 | ENSMUSG00000035150 | 9.054230 | 1.757601 | 1.035163e-16 | ENSMUSG00000029591 | 7.549578 | 1.644684 | 3.177064e-11 | ENSMUSG00000034932 | 5.641506 | 0.921757 | 7.998588e-06 | ENSMUSG00000022837 | 9.974640 | 3.170303 | 1.263488e-20 | ENSMUSG00000032387 | 9.920895 | 3.214711 | 2.541836e-20 | ENSMUSG00000022432 | 11.149601 | 3.171072 | 2.453094e-26 | ENSMUSG00000020372 | 8.320822 | 1.135716 | 6.150679e-14 | ENSMUSG00000012848 | 6.928813 | 1.572026 | 2.988092e-09 | ENSMUSG00000013415 | 8.849686 | 1.755305 | 6.841699e-16 | ENSMUSG00000056201 | 7.643992 | 1.235667 | 1.641613e-11 | ENSMUSG00000052374 | 7.439526 | 2.309771 | 7.605369e-11 | ENSMUSG00000032399 | 9.472212 | 0.784287 | 1.616260e-18 | ENSMUSG00000073102 | 9.748669 | 3.542623 | 1.456981e-19 | ENSMUSG00000053398 | 12.593401 | 3.908803 | 1.431629e-33 |
| 28 | ENSMUSG00000032518 | 8.147474 | 1.053758 | 2.317433e-13 | ENSMUSG00000024608 | 9.615305 | 1.228392 | 5.075292e-19 | ENSMUSG00000063382 | 9.022331 | 2.710283 | 1.339657e-16 | ENSMUSG00000028640 | 7.545193 | 1.939182 | 3.179829e-11 | ENSMUSG00000079641 | 5.613514 | 1.335702 | 8.321906e-06 | ENSMUSG00000024786 | 9.932040 | 3.540508 | 1.883436e-20 | ENSMUSG00000030291 | 9.879769 | 2.149617 | 3.586699e-20 | ENSMUSG00000036499 | 11.128684 | 3.337958 | 3.008452e-26 | ENSMUSG00000037188 | 8.308484 | 2.370989 | 6.497343e-14 | ENSMUSG00000057278 | 6.918343 | 1.964693 | 3.116886e-09 | ENSMUSG00000055065 | 8.840087 | 1.610833 | 7.198736e-16 | ENSMUSG00000033845 | 7.559332 | 1.375842 | 3.049346e-11 | ENSMUSG00000038845 | 7.404009 | 1.229870 | 9.613591e-11 | ENSMUSG00000030357 | 9.468452 | 1.372171 | 1.631390e-18 | ENSMUSG00000032047 | 9.696410 | 2.143185 | 2.350499e-19 | ENSMUSG00000007564 | 12.577294 | 2.377677 | 1.706786e-33 |
| 29 | ENSMUSG00000027547 | 8.141083 | 1.977915 | 2.375252e-13 | ENSMUSG00000071528 | 9.614039 | 1.834906 | 5.075292e-19 | ENSMUSG00000025332 | 8.997754 | 1.819495 | 1.622119e-16 | ENSMUSG00000028333 | 7.531411 | 1.318298 | 3.416978e-11 | ENSMUSG00000025920 | 5.603956 | 2.145765 | 8.468653e-06 | ENSMUSG00000051977 | 9.919511 | 3.911473 | 2.032783e-20 | ENSMUSG00000028212 | 9.876606 | 2.765891 | 3.586699e-20 | ENSMUSG00000006941 | 11.103060 | 1.721562 | 3.891011e-26 | ENSMUSG00000029581 | 8.306903 | 2.275014 | 6.497343e-14 | ENSMUSG00000020460 | 6.880824 | 1.592141 | 3.935976e-09 | ENSMUSG00000062825 | 8.808090 | 1.581099 | 9.261646e-16 | ENSMUSG00000102252 | 7.548408 | 1.742796 | 3.205738e-11 | ENSMUSG00000031353 | 7.283897 | 1.293793 | 2.283477e-10 | ENSMUSG00000051965 | 9.460309 | 3.437526 | 1.718373e-18 | ENSMUSG00000053909 | 9.598425 | 2.753197 | 5.906040e-19 | ENSMUSG00000027602 | 12.558504 | 2.307501 | 2.106130e-33 |
| 30 | ENSMUSG00000031826 | 8.136609 | 1.766640 | 2.398037e-13 | ENSMUSG00000078974 | 9.609612 | 1.558262 | 5.127482e-19 | ENSMUSG00000070733 | 8.978144 | 1.954974 | 1.878300e-16 | ENSMUSG00000033845 | 7.527652 | 1.245281 | 3.416978e-11 | ENSMUSG00000035048 | 5.548653 | 0.809555 | 1.102372e-05 | ENSMUSG00000031715 | 9.918884 | 2.322992 | 2.032783e-20 | ENSMUSG00000017707 | 9.845604 | 2.668066 | 4.626007e-20 | ENSMUSG00000022295 | 11.074299 | 1.966723 | 5.288555e-26 | ENSMUSG00000024406 | 8.250593 | 1.771610 | 9.916722e-14 | ENSMUSG00000032330 | 6.833706 | 1.706198 | 5.255476e-09 | ENSMUSG00000024420 | 8.751296 | 1.942124 | 1.485051e-15 | ENSMUSG00000041891 | 7.482864 | 1.680250 | 5.120177e-11 | ENSMUSG00000054612 | 7.264847 | 1.715909 | 2.547356e-10 | ENSMUSG00000071341 | 9.440888 | 3.372850 | 2.016991e-18 | ENSMUSG00000025357 | 9.564674 | 2.725935 | 7.924946e-19 | ENSMUSG00000029634 | 12.547229 | 2.804089 | 2.364577e-33 |
| 31 | ENSMUSG00000021938 | 8.125105 | 1.673141 | 2.567405e-13 | ENSMUSG00000008683 | 9.569752 | 1.510148 | 6.975404e-19 | ENSMUSG00000015165 | 8.916700 | 1.606960 | 3.177410e-16 | ENSMUSG00000030127 | 7.481293 | 1.091225 | 4.724570e-11 | ENSMUSG00000051159 | 5.506665 | 1.613639 | 1.330341e-05 | ENSMUSG00000032409 | 9.913873 | 3.000286 | 2.040569e-20 | ENSMUSG00000038187 | 9.844972 | 2.701699 | 4.626007e-20 | ENSMUSG00000060771 | 11.065932 | 4.139163 | 5.642558e-26 | ENSMUSG00000042348 | 8.247113 | 1.986505 | 9.916722e-14 | ENSMUSG00000090862 | 6.795315 | 2.253257 | 6.552724e-09 | ENSMUSG00000101609 | 8.733965 | 1.884725 | 1.677251e-15 | ENSMUSG00000033751 | 7.442583 | 1.412540 | 6.564459e-11 | ENSMUSG00000029430 | 7.218675 | 0.828643 | 3.472257e-10 | ENSMUSG00000005413 | 9.384192 | 3.126902 | 3.375209e-18 | ENSMUSG00000006057 | 9.538546 | 1.169089 | 9.880095e-19 | ENSMUSG00000025487 | 12.510721 | 1.873924 | 3.650667e-33 |
| 32 | ENSMUSG00000006356 | 8.112323 | 1.901815 | 2.724186e-13 | ENSMUSG00000069309 | 9.568486 | 3.214732 | 6.975404e-19 | ENSMUSG00000046062 | 8.912517 | 1.807629 | 3.202607e-16 | ENSMUSG00000030541 | 7.473462 | 1.946297 | 4.871277e-11 | ENSMUSG00000031383 | 5.420298 | 1.411387 | 2.061435e-05 | ENSMUSG00000031010 | 9.913246 | 2.208581 | 2.040569e-20 | ENSMUSG00000023832 | 9.823461 | 2.207152 | 5.564456e-20 | ENSMUSG00000035540 | 11.019392 | 4.023561 | 9.341166e-26 | ENSMUSG00000012396 | 8.246164 | 2.168669 | 9.916722e-14 | ENSMUSG00000092203 | 6.773501 | 2.693801 | 7.415420e-09 | ENSMUSG00000022443 | 8.710501 | 1.765680 | 1.940703e-15 | ENSMUSG00000059552 | 7.441900 | 1.903165 | 6.564459e-11 | ENSMUSG00000034341 | 7.208988 | 1.531767 | 3.515340e-10 | ENSMUSG00000059447 | 9.372603 | 1.856373 | 3.677604e-18 | ENSMUSG00000019139 | 9.451447 | 1.730328 | 2.210318e-18 | ENSMUSG00000025401 | 12.485488 | 6.166241 | 4.888850e-33 |
| 33 | ENSMUSG00000020721 | 8.111684 | 1.865416 | 2.724186e-13 | ENSMUSG00000050856 | 9.567221 | 1.704452 | 6.975404e-19 | ENSMUSG00000059273 | 8.907810 | 1.995704 | 3.246013e-16 | ENSMUSG00000020349 | 7.456861 | 1.974145 | 5.372363e-11 | ENSMUSG00000016308 | 5.405277 | 1.078252 | 2.206853e-05 | ENSMUSG00000022940 | 9.891946 | 2.949537 | 2.461928e-20 | ENSMUSG00000026277 | 9.802582 | 2.959312 | 6.638305e-20 | ENSMUSG00000009670 | 10.983832 | 2.860775 | 1.312806e-25 | ENSMUSG00000024036 | 8.233827 | 2.273760 | 1.069675e-13 | ENSMUSG00000046330 | 6.759541 | 1.934832 | 7.841314e-09 | ENSMUSG00000005161 | 8.708901 | 1.741498 | 1.940703e-15 | ENSMUSG00000021963 | 7.396156 | 1.043512 | 8.999340e-11 | ENSMUSG00000025739 | 7.196719 | 1.772803 | 3.739621e-10 | ENSMUSG00000056155 | 9.335642 | 2.789213 | 5.095413e-18 | ENSMUSG00000029223 | 9.443282 | 2.134849 | 2.319313e-18 | ENSMUSG00000026809 | 12.394754 | 5.108266 | 1.449932e-32 |
| 34 | ENSMUSG00000017499 | 8.070780 | 2.396002 | 3.718605e-13 | ENSMUSG00000031939 | 9.565323 | 2.116014 | 6.975404e-19 | ENSMUSG00000013415 | 8.827803 | 1.795891 | 6.297537e-16 | ENSMUSG00000002870 | 7.399852 | 1.602194 | 8.042729e-11 | ENSMUSG00000036199 | 5.395719 | 0.640289 | 2.241259e-05 | ENSMUSG00000053012 | 9.823661 | 2.598363 | 4.740699e-20 | ENSMUSG00000037315 | 9.800051 | 3.168722 | 6.638305e-20 | ENSMUSG00000073411 | 10.968668 | 3.795732 | 1.500853e-25 | ENSMUSG00000031826 | 8.207570 | 1.775124 | 1.296464e-13 | ENSMUSG00000046364 | 6.757796 | 1.699039 | 7.841314e-09 | ENSMUSG00000042406 | 8.707301 | 1.671174 | 1.940703e-15 | ENSMUSG00000024837 | 7.357240 | 2.100099 | 1.170874e-10 | ENSMUSG00000074397 | 7.190261 | 1.711933 | 3.785402e-10 | ENSMUSG00000030007 | 9.322486 | 1.201689 | 5.637622e-18 | ENSMUSG00000032380 | 9.418242 | 2.928796 | 2.860606e-18 | ENSMUSG00000038375 | 12.308584 | 3.489920 | 3.950145e-32 |
| 35 | ENSMUSG00000015112 | 8.030516 | 1.908452 | 5.044323e-13 | ENSMUSG00000073702 | 9.531790 | 1.503407 | 9.373117e-19 | ENSMUSG00000049539 | 8.813684 | 2.471850 | 6.955918e-16 | ENSMUSG00000034330 | 7.391082 | 1.801297 | 8.365438e-11 | ENSMUSG00000068523 | 5.395719 | 0.609472 | 2.241259e-05 | ENSMUSG00000027722 | 9.810505 | 2.591710 | 5.272754e-20 | ENSMUSG00000027509 | 9.786764 | 1.678593 | 7.371625e-20 | ENSMUSG00000025037 | 10.914284 | 3.518442 | 2.654354e-25 | ENSMUSG00000013089 | 8.204407 | 1.991545 | 1.296929e-13 | ENSMUSG00000071415 | 6.745580 | 1.857625 | 8.316997e-09 | ENSMUSG00000024393 | 8.681170 | 1.226289 | 2.365616e-15 | ENSMUSG00000030127 | 7.292379 | 1.184795 | 1.846373e-10 | ENSMUSG00000078812 | 7.187678 | 0.756343 | 3.785402e-10 | ENSMUSG00000010796 | 9.292728 | 2.308932 | 7.004442e-18 | ENSMUSG00000073460 | 9.406266 | 1.914644 | 3.116867e-18 | ENSMUSG00000039661 | 12.299994 | 4.779174 | 4.298035e-32 |
| 36 | ENSMUSG00000029581 | 8.011342 | 2.182590 | 5.759461e-13 | ENSMUSG00000041453 | 9.521667 | 1.389832 | 1.005356e-18 | ENSMUSG00000058881 | 8.782569 | 2.065274 | 8.718931e-16 | ENSMUSG00000028851 | 7.364143 | 1.095649 | 9.710709e-11 | ENSMUSG00000025289 | 5.391623 | 0.692991 | 2.241259e-05 | ENSMUSG00000025001 | 9.784193 | 2.093386 | 6.530281e-20 | ENSMUSG00000027379 | 9.772845 | 2.347879 | 8.241517e-20 | ENSMUSG00000113902 | 10.862775 | 2.251386 | 4.452668e-25 | ENSMUSG00000003153 | 8.196815 | 2.529436 | 1.337936e-13 | ENSMUSG00000022114 | 6.715042 | 3.093367 | 9.770844e-09 | ENSMUSG00000053332 | 8.678504 | 1.509174 | 2.365616e-15 | ENSMUSG00000032171 | 7.278724 | 1.202968 | 1.987975e-10 | ENSMUSG00000006299 | 7.120519 | 1.173492 | 5.587804e-10 | ENSMUSG00000019471 | 9.292415 | 1.445163 | 7.004442e-18 | ENSMUSG00000025081 | 9.400823 | 2.538855 | 3.193715e-18 | ENSMUSG00000029762 | 12.268587 | 4.068056 | 5.949600e-32 |
| 37 | ENSMUSG00000058558 | 8.006229 | 0.989652 | 5.867465e-13 | ENSMUSG00000113061 | 9.513442 | 1.482078 | 1.059504e-18 | ENSMUSG00000038587 | 8.753023 | 1.935407 | 1.105784e-15 | ENSMUSG00000020307 | 7.361011 | 1.331875 | 9.710709e-11 | ENSMUSG00000046814 | 5.354072 | 2.401212 | 2.620289e-05 | ENSMUSG00000032387 | 9.781688 | 3.193652 | 6.545273e-20 | ENSMUSG00000025630 | 9.762722 | 2.373599 | 8.879649e-20 | ENSMUSG00000028957 | 10.846826 | 3.353287 | 5.240303e-25 | ENSMUSG00000063229 | 8.192386 | 1.827090 | 1.337936e-13 | ENSMUSG00000037805 | 6.698463 | 1.274586 | 1.069130e-08 | ENSMUSG00000030795 | 8.667306 | 1.219907 | 2.541502e-15 | ENSMUSG00000028832 | 7.267118 | 1.148532 | 2.086697e-10 | ENSMUSG00000025403 | 7.044318 | 1.908357 | 9.460309e-10 | ENSMUSG00000028345 | 9.277380 | 1.639366 | 7.898522e-18 | ENSMUSG00000030007 | 9.387214 | 1.039139 | 3.538764e-18 | ENSMUSG00000009941 | 12.263755 | 4.680332 | 6.067684e-32 |
| 38 | ENSMUSG00000005360 | 7.965645 | 2.006265 | 7.972394e-13 | ENSMUSG00000079435 | 9.457132 | 1.486953 | 1.748095e-18 | ENSMUSG00000026421 | 8.740474 | 2.206711 | 1.206387e-15 | ENSMUSG00000028766 | 7.305255 | 1.829136 | 1.372822e-10 | ENSMUSG00000006369 | 5.350658 | 1.585433 | 2.620289e-05 | ENSMUSG00000025630 | 9.764772 | 2.560727 | 7.566808e-20 | ENSMUSG00000025480 | 9.730454 | 3.166425 | 1.190322e-19 | ENSMUSG00000043410 | 10.824862 | 3.211410 | 6.510204e-25 | ENSMUSG00000062070 | 8.191753 | 1.804410 | 1.337936e-13 | ENSMUSG00000079259 | 6.689738 | 2.560926 | 1.109063e-08 | ENSMUSG00000026276 | 8.663039 | 1.941446 | 2.541990e-15 | ENSMUSG00000036371 | 7.265069 | 1.023036 | 2.086697e-10 | ENSMUSG00000070493 | 7.037215 | 0.775652 | 9.728899e-10 | ENSMUSG00000074397 | 9.274247 | 2.212958 | 7.968065e-18 | ENSMUSG00000022652 | 9.352374 | 2.456949 | 4.796442e-18 | ENSMUSG00000033075 | 12.256239 | 2.876153 | 6.529398e-32 |
| 39 | ENSMUSG00000022353 | 7.951904 | 1.856180 | 8.714981e-13 | ENSMUSG00000030744 | 9.455868 | 1.262307 | 1.748095e-18 | ENSMUSG00000035168 | 8.719818 | 2.001949 | 1.382294e-15 | ENSMUSG00000028884 | 7.293353 | 1.282826 | 1.466463e-10 | ENSMUSG00000055817 | 5.341441 | 1.196445 | 2.683578e-05 | ENSMUSG00000039105 | 9.762267 | 1.546043 | 7.591148e-20 | ENSMUSG00000022429 | 9.726342 | 3.647375 | 1.209913e-19 | ENSMUSG00000062170 | 10.742764 | 2.910783 | 1.472675e-24 | ENSMUSG00000060206 | 8.172772 | 2.081483 | 1.529832e-13 | ENSMUSG00000079435 | 6.674032 | 1.865432 | 1.155758e-08 | ENSMUSG00000091405 | 8.661440 | 1.977121 | 2.541990e-15 | ENSMUSG00000042367 | 7.230250 | 2.043980 | 2.630737e-10 | ENSMUSG00000019139 | 6.992012 | 1.500457 | 1.314338e-09 | ENSMUSG00000070319 | 9.220998 | 1.215600 | 1.259754e-17 | ENSMUSG00000051965 | 9.332233 | 3.565436 | 5.518684e-18 | ENSMUSG00000036526 | 12.225904 | 5.930776 | 9.309468e-32 |
| 40 | ENSMUSG00000040511 | 7.946791 | 2.234810 | 8.888925e-13 | ENSMUSG00000045128 | 9.445745 | 1.053977 | 1.878650e-18 | ENSMUSG00000011256 | 8.704391 | 2.032947 | 1.548667e-15 | ENSMUSG00000003528 | 7.280823 | 1.451314 | 1.574342e-10 | ENSMUSG00000028470 | 5.337686 | 1.167193 | 2.703689e-05 | ENSMUSG00000030884 | 9.742846 | 2.042310 | 9.000506e-20 | ENSMUSG00000026027 | 9.708943 | 3.032785 | 1.401929e-19 | ENSMUSG00000025586 | 10.727598 | 2.564767 | 1.699669e-24 | ENSMUSG00000105039 | 8.163281 | 2.125829 | 1.617393e-13 | ENSMUSG00000058600 | 6.605974 | 1.809842 | 1.626363e-08 | ENSMUSG00000024949 | 8.654507 | 1.246927 | 2.635459e-15 | ENSMUSG00000004151 | 7.224105 | 4.161970 | 2.685322e-10 | ENSMUSG00000068856 | 6.971347 | 1.175153 | 1.489553e-09 | ENSMUSG00000038845 | 9.189048 | 1.471134 | 1.641757e-17 | ENSMUSG00000019179 | 9.326245 | 1.254864 | 5.700380e-18 | ENSMUSG00000036598 | 12.215972 | 4.181101 | 1.020397e-31 |
| 41 | ENSMUSG00000059436 | 7.916752 | 1.818874 | 1.085980e-12 | ENSMUSG00000003429 | 9.442581 | 1.123376 | 1.890157e-18 | ENSMUSG00000030795 | 8.686873 | 1.226509 | 1.767756e-15 | ENSMUSG00000021270 | 7.267041 | 0.849673 | 1.706444e-10 | ENSMUSG00000023106 | 5.321983 | 0.822359 | 2.857288e-05 | ENSMUSG00000005470 | 9.715281 | 2.813024 | 1.156075e-19 | ENSMUSG00000024786 | 9.686799 | 3.814904 | 1.702075e-19 | ENSMUSG00000001948 | 10.711126 | 2.418876 | 1.989945e-24 | ENSMUSG00000062300 | 8.144934 | 1.934803 | 1.810116e-13 | ENSMUSG00000017404 | 6.564965 | 1.147014 | 2.103679e-08 | ENSMUSG00000028184 | 8.587848 | 2.096848 | 4.605162e-15 | ENSMUSG00000020358 | 7.190650 | 1.484769 | 3.351137e-10 | ENSMUSG00000006057 | 6.964890 | 1.026076 | 1.526320e-09 | ENSMUSG00000052833 | 9.180277 | 1.260312 | 1.738008e-17 | ENSMUSG00000031880 | 9.314814 | 3.504511 | 6.201246e-18 | ENSMUSG00000063065 | 12.215435 | 2.732449 | 1.020397e-31 |
| 42 | ENSMUSG00000057666 | 7.907166 | 1.639737 | 1.149475e-12 | ENSMUSG00000053332 | 9.435621 | 2.113323 | 1.972977e-18 | ENSMUSG00000060126 | 8.626475 | 1.260788 | 2.938367e-15 | ENSMUSG00000024837 | 7.255138 | 2.028458 | 1.824614e-10 | ENSMUSG00000022551 | 5.320617 | 0.848255 | 2.857288e-05 | ENSMUSG00000055943 | 9.710270 | 2.712387 | 1.190070e-19 | ENSMUSG00000024269 | 9.645675 | 2.680064 | 2.432906e-19 | ENSMUSG00000023020 | 10.694131 | 1.785349 | 2.343301e-24 | ENSMUSG00000058558 | 8.144301 | 0.988555 | 1.810116e-13 | ENSMUSG00000060938 | 6.529191 | 1.429305 | 2.577372e-08 | ENSMUSG00000045136 | 8.575049 | 2.337433 | 5.027542e-15 | ENSMUSG00000051695 | 7.182457 | 1.806479 | 3.475533e-10 | ENSMUSG00000028964 | 6.957786 | 0.900788 | 1.571825e-09 | ENSMUSG00000056076 | 9.122642 | 2.004494 | 2.857576e-17 | ENSMUSG00000007564 | 9.302837 | 1.666678 | 6.783552e-18 | ENSMUSG00000024033 | 12.195571 | 2.663794 | 1.265073e-31 |
| 43 | ENSMUSG00000062300 | 7.896301 | 1.917266 | 1.201532e-12 | ENSMUSG00000038900 | 9.416640 | 1.850511 | 2.310442e-18 | ENSMUSG00000089774 | 8.617585 | 2.032177 | 3.109474e-15 | ENSMUSG00000042367 | 7.161168 | 1.927673 | 3.563273e-10 | ENSMUSG00000018770 | 5.308328 | 0.597227 | 2.944930e-05 | ENSMUSG00000062773 | 9.682705 | 3.790229 | 1.528481e-19 | ENSMUSG00000024758 | 9.643144 | 2.416534 | 2.440603e-19 | ENSMUSG00000079317 | 10.673737 | 2.986423 | 2.890632e-24 | ENSMUSG00000069601 | 8.133546 | 1.890131 | 1.936165e-13 | ENSMUSG00000040952 | 6.489927 | 1.411174 | 3.177048e-08 | ENSMUSG00000101355 | 8.569183 | 2.081968 | 5.125918e-15 | ENSMUSG00000005566 | 7.144224 | 1.528278 | 4.489700e-10 | ENSMUSG00000015869 | 6.950037 | 1.370582 | 1.626739e-09 | ENSMUSG00000020432 | 9.093198 | 2.870163 | 3.682557e-17 | ENSMUSG00000025651 | 9.272898 | 1.198288 | 8.786862e-18 | ENSMUSG00000009090 | 12.195034 | 2.927677 | 1.265073e-31 |
| 44 | ENSMUSG00000023393 | 7.896301 | 2.391323 | 1.201532e-12 | ENSMUSG00000074578 | 9.407783 | 2.427437 | 2.457777e-18 | ENSMUSG00000022390 | 8.595099 | 2.018835 | 3.664920e-15 | ENSMUSG00000004642 | 7.111050 | 1.385396 | 5.027345e-10 | ENSMUSG00000018585 | 5.252343 | 0.903922 | 3.767706e-05 | ENSMUSG00000031158 | 9.679573 | 2.520765 | 1.544701e-19 | ENSMUSG00000032065 | 9.629857 | 2.849061 | 2.719852e-19 | ENSMUSG00000005881 | 10.642884 | 2.762885 | 3.950099e-24 | ENSMUSG00000022353 | 8.110768 | 1.823349 | 2.287313e-13 | ENSMUSG00000060475 | 6.448045 | 2.141384 | 3.925462e-08 | ENSMUSG00000020422 | 8.567583 | 2.330751 | 5.125918e-15 | ENSMUSG00000047751 | 7.131935 | 1.718151 | 4.800381e-10 | ENSMUSG00000024966 | 6.937121 | 0.972735 | 1.712648e-09 | ENSMUSG00000021273 | 9.088186 | 1.779879 | 3.725571e-17 | ENSMUSG00000049354 | 9.270176 | 1.651214 | 8.818053e-18 | ENSMUSG00000018322 | 12.192349 | 2.122135 | 1.284909e-31 |
| 45 | ENSMUSG00000081683 | 7.894383 | 2.773076 | 1.201532e-12 | ENSMUSG00000028792 | 9.403354 | 1.780985 | 2.507772e-18 | ENSMUSG00000045817 | 8.594053 | 2.259722 | 3.664920e-15 | ENSMUSG00000017221 | 7.065318 | 1.359621 | 6.858339e-10 | ENSMUSG00000002416 | 5.236299 | 1.124020 | 4.010108e-05 | ENSMUSG00000035439 | 9.677693 | 2.849065 | 1.544701e-19 | ENSMUSG00000002076 | 9.621633 | 2.619258 | 2.886275e-19 | ENSMUSG00000039047 | 10.620921 | 2.738357 | 4.951597e-24 | ENSMUSG00000026380 | 8.077868 | 2.012314 | 2.892131e-13 | ENSMUSG00000038274 | 6.444555 | 1.125510 | 3.954097e-08 | ENSMUSG00000026193 | 8.488126 | 1.981649 | 9.964344e-15 | ENSMUSG00000030057 | 7.128521 | 1.016621 | 4.813953e-10 | ENSMUSG00000064264 | 6.914520 | 1.162945 | 1.970520e-09 | ENSMUSG00000028452 | 9.070019 | 1.257069 | 4.328901e-17 | ENSMUSG00000025403 | 9.207030 | 2.118877 | 1.557017e-17 | ENSMUSG00000054003 | 12.176243 | 4.104885 | 1.539018e-31 |
| 46 | ENSMUSG00000038900 | 7.891188 | 1.582290 | 1.209878e-12 | ENSMUSG00000002477 | 9.373618 | 1.732065 | 3.254921e-18 | ENSMUSG00000101355 | 8.565292 | 2.161820 | 4.566236e-15 | ENSMUSG00000002985 | 7.037753 | 2.041459 | 8.200424e-10 | ENSMUSG00000022913 | 5.234591 | 0.815929 | 4.010108e-05 | ENSMUSG00000030180 | 9.636347 | 2.463068 | 2.269639e-19 | ENSMUSG00000028617 | 9.618468 | 2.490311 | 2.916892e-19 | ENSMUSG00000071470 | 10.618830 | 3.190751 | 4.969151e-24 | ENSMUSG00000057666 | 8.077236 | 1.628011 | 2.892131e-13 | ENSMUSG00000025508 | 6.427104 | 1.848196 | 4.261342e-08 | ENSMUSG00000002329 | 8.456662 | 1.922969 | 1.277758e-14 | ENSMUSG00000058135 | 7.117597 | 1.754825 | 5.068793e-10 | ENSMUSG00000009894 | 6.896439 | 1.640461 | 2.195981e-09 | ENSMUSG00000031781 | 9.051851 | 1.662172 | 5.029692e-17 | ENSMUSG00000038803 | 9.194238 | 1.242507 | 1.708036e-17 | ENSMUSG00000037570 | 12.171947 | 1.984214 | 1.595160e-31 |
| 47 | ENSMUSG00000025967 | 7.884158 | 1.135527 | 1.243768e-12 | ENSMUSG00000017404 | 9.356535 | 0.973381 | 3.746714e-18 | ENSMUSG00000014498 | 8.482409 | 2.175339 | 8.852729e-15 | ENSMUSG00000027203 | 7.033994 | 1.199811 | 8.265571e-10 | ENSMUSG00000016637 | 5.231519 | 1.308357 | 4.032541e-05 | ENSMUSG00000027855 | 9.630709 | 3.217265 | 2.354106e-19 | ENSMUSG00000050705 | 9.613407 | 2.105385 | 3.003874e-19 | ENSMUSG00000034210 | 10.589545 | 2.820344 | 6.734162e-24 | ENSMUSG00000023004 | 8.063950 | 1.729181 | 3.161416e-13 | ENSMUSG00000007892 | 6.426232 | 1.532440 | 4.261342e-08 | ENSMUSG00000060206 | 8.451863 | 1.778304 | 1.303669e-14 | ENSMUSG00000024151 | 7.115549 | 1.419635 | 5.068793e-10 | ENSMUSG00000054196 | 6.871899 | 2.809532 | 2.560738e-09 | ENSMUSG00000021037 | 9.045587 | 1.396583 | 5.240736e-17 | ENSMUSG00000023966 | 9.192605 | 2.888978 | 1.708036e-17 | ENSMUSG00000040629 | 12.161747 | 3.169278 | 1.777813e-31 |
| 48 | ENSMUSG00000024406 | 7.849645 | 1.747446 | 1.597645e-12 | ENSMUSG00000046364 | 9.346412 | 1.551781 | 4.038948e-18 | ENSMUSG00000101609 | 8.481101 | 2.140858 | 8.852729e-15 | ENSMUSG00000020717 | 7.016767 | 1.871361 | 9.177922e-10 | ENSMUSG00000027404 | 5.197041 | 0.743601 | 4.701771e-05 | ENSMUSG00000000088 | 9.626323 | 1.490817 | 2.412859e-19 | ENSMUSG00000002833 | 9.568486 | 2.499890 | 4.522635e-19 | ENSMUSG00000021098 | 10.559999 | 2.372829 | 9.144557e-24 | ENSMUSG00000099583 | 8.050663 | 1.674619 | 3.456502e-13 | ENSMUSG00000032607 | 6.368644 | 2.865632 | 5.862736e-08 | ENSMUSG00000020812 | 8.440131 | 1.774861 | 1.411996e-14 | ENSMUSG00000027091 | 7.093701 | 1.261710 | 5.816319e-10 | ENSMUSG00000002996 | 6.827342 | 1.427866 | 3.432893e-09 | ENSMUSG00000063888 | 9.039322 | 1.367232 | 5.461845e-17 | ENSMUSG00000045503 | 9.188522 | 1.859362 | 1.721876e-17 | ENSMUSG00000026879 | 12.035580 | 4.920218 | 8.132534e-31 |
| 49 | ENSMUSG00000001918 | 7.834306 | 2.044502 | 1.774152e-12 | ENSMUSG00000038274 | 9.341983 | 1.123650 | 4.127324e-18 | ENSMUSG00000037857 | 8.469074 | 1.767935 | 9.640974e-15 | ENSMUSG00000040945 | 6.992647 | 1.446076 | 1.070503e-09 | ENSMUSG00000024661 | 5.192261 | 0.491810 | 4.732284e-05 | ENSMUSG00000022034 | 9.619745 | 2.388381 | 2.527124e-19 | ENSMUSG00000031352 | 9.567221 | 2.497653 | 4.522635e-19 | ENSMUSG00000068823 | 10.524179 | 1.642215 | 1.302630e-23 | ENSMUSG00000046434 | 8.029784 | 1.826028 | 4.021282e-13 | ENSMUSG00000024067 | 6.353811 | 2.046960 | 6.253170e-08 | ENSMUSG00000019979 | 8.384137 | 1.719651 | 2.230686e-14 | ENSMUSG00000039660 | 7.082777 | 1.755544 | 6.168040e-10 | ENSMUSG00000062867 | 6.818301 | 1.029719 | 3.590679e-09 | ENSMUSG00000022300 | 9.019275 | 1.688904 | 6.357997e-17 | ENSMUSG00000027510 | 9.187433 | 1.950811 | 1.721876e-17 | ENSMUSG00000023967 | 12.031821 | 1.855927 | 8.376291e-31 |
In [135]:
DE_female_E105 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E10.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E115 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E11.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E125 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E12.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E135 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E13.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E145 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E14.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E155 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E15.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E165 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E16.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
DE_female_E165vE135 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E16.5", key="female", pval_cutoff=0.05, log2fc_min=1.588, gene_symbols="external_gene_name")
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
In [136]:
sc.tl.rank_genes_groups(gcAnndata_germ, 'stage_sex', method='wilcoxon', use_raw=False)
ranking genes
finished: added to `.uns['rank_genes_groups']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)
In [137]:
sc.pl.rank_genes_groups(gcAnndata_germ, n_genes=25, sharey=False, gene_symbols='external_gene_name',)
In [138]:
gcAnndata_germ
Out[138]:
AnnData object with n_obs × n_vars = 774 × 21827
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden', 'Chr X', 'Chr 9', 'dpt_pseudotime', 'dpt_groups', 'dpt_order', 'dpt_order_indices'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors', 'paga', 'stage_sex_sizes', 'iroot', 'diffmap_evals', 'dpt_changepoints', 'dpt_grouptips', 'female', 'male', 'rank_genes_groups'
obsm: 'X_pca', 'X_umap', 'X_tsne', 'X_diffmap'
varm: 'PCs'
obsp: 'distances', 'connectivities'
In [139]:
gslist = {}
for i in ['male_E9.5', 'female_E9.5', 'male_E10.5', 'female_E10.5', 'male_E11.5', 'female_E11.5', 'male_E12.5', 'female_E12.5', 'male_E13.5', 'female_E13.5', 'male_E14.5', 'female_E14.5', 'male_E15.5', 'female_E15.5', 'male_E16.5', 'female_E16.5']:
genes = sc.get.rank_genes_groups_df(gcAnndata,
group = i,
pval_cutoff=0.01,
log2fc_min=2,
gene_symbols="external_gene_name",
key="rank_genes_groups").sort_values(by=["scores"],
ascending=[False]).dropna(axis=0)["external_gene_name"].to_list()
gslist[i] = genes
In [ ]:
In [140]:
top10 = pd.Series(gslist['male_E11.5'][:10] + gslist['male_E12.5'][:10] + gslist['male_E13.5'][:10] + gslist['male_E14.5'][:10] + gslist['male_E15.5'][:10] + gslist['male_E16.5'][:10] + gslist['female_E11.5'][:10] + gslist['female_E12.5'][:10] + gslist['female_E13.5'][:10] + gslist['female_E14.5'][:10] + gslist['female_E15.5'][:10] + gslist['female_E16.5'][:10], name = 'top10')
top10.drop_duplicates()
Out[140]:
0 Uty
1 Vdac1
2 Hnrnpa3
3 Dlc1
4 Dpysl2
...
115 H1f0
116 Sycp3
117 Ccnb3
118 Spo11
119 1700029J07Rik
Name: top10, Length: 107, dtype: object
In [141]:
sns.set_context("paper", font_scale=1, rc={"font.size":7,"axes.labelsize":7,"axes.titlesize":14})
d = sc.pl.matrixplot(gcAnndata_germ,
top10.drop_duplicates(),
cmap = heatcolors_wr,
groupby = "stage_sex",
standard_scale="var",
gene_symbols='external_gene_name',
edgecolor="white",
linewidths=-0,
swap_axes = True,
figsize = [4,9]#,
#save = "male_female_top10_horiz.pdf",
)
In [142]:
gcAnndata_germ
Out[142]:
AnnData object with n_obs × n_vars = 774 × 21827
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden', 'Chr X', 'Chr 9', 'dpt_pseudotime', 'dpt_groups', 'dpt_order', 'dpt_order_indices'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors', 'paga', 'stage_sex_sizes', 'iroot', 'diffmap_evals', 'dpt_changepoints', 'dpt_grouptips', 'female', 'male', 'rank_genes_groups'
obsm: 'X_pca', 'X_umap', 'X_tsne', 'X_diffmap'
varm: 'PCs'
obsp: 'distances', 'connectivities'